phd in canada computer science

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Computer Science (PhD)

Program description.

The Doctor of Philosophy (Ph.D.) in Computer Science offered by the School of Computer Science in the Faculty of Science is a research-intensive program that emphasizes stimulating and engaging learning opportunities. The program's objective is to equip students with skills in original thinking, information synthesis, and scientific communication to pursue professional opportunities in academia or industry.

Unique Program Features

• The program trains students to become strong, independent researchers in the field of their choice;
• Students will be exposed to cutting-edge computer science developments. The School’s Faculty members conduct research in artificial intelligence, robotics, machine learning and vision, bioinformatics, systems research and CS Education, software engineering, programming languages, and foundations of computer science;
• The School is one of the leading teaching and research centres for computer science in Canada;
• Graduates pursue careers in industry or in academic positions at universities and research labs.

University-Level Admission Requirements

• An eligible Bachelor's degree with a minimum 3.0 GPA out of a possible 4.0 GPA
• English-language proficiency

Each program has specific admission requirements including required application documents. Please visit the program website for more details.

Visit our Educational credentials and grade equivalencies and English language proficiency webpages for additional information.

Program Website

PhD in Computer Science website

Department Contact

Graduate Program grad.cs [at] mcgill.ca (subject: PhD%20in%20Computer%20Science) (email)

Available Intakes

Application deadlines.

Note : Application deadlines are subject to change without notice. Please check the application portal for the most up-to-date information.

Application Resources

• Application Steps webpage
• Submit Your Application webpage
• Connecting with a supervisor webpage
• Graduate Funding webpage

Application Workshops

Consult our full list of our virtual application-focused workshops on the Events webpage.

Department and University Information

Graduate and postdoctoral studies.

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Computer Sciences in Canada

48  Computer Sciences PhDs in Canada

Electrical and Computer Engineering - Nanotechnology Admission to the Electrical and Computer Engineering - Nanotechnology program at University of Waterloo is... University of Waterloo Waterloo, Ontario, Canada

Computer Science The Computer Science PhD program at the University of Toronto provides advanced depth and breadth of... University of Toronto Toronto, Ontario, Canada

Information Studies The Doctor of Philosophy (Ph.D.) in Information Studies offered by the School of Information Studies in the... Faculty of Arts Montréal, Quebec, Canada

Computing Science The School of Computing Science at Simon Fraser University offers programs leading to the M.Sc. and PhD... Simon Fraser University Burnaby, British Columbia, Canada

Electrical and Computer Engineering McMaster University’s Electrical and Computer Engineering department is ranked as one of the best departments... McMaster University Hamilton, Ontario, Canada

Electrical and Computer Engineering Admission to the Electrical and Computer Engineering program from University of Waterloo is based upon... University of Waterloo Waterloo, Ontario, Canada

Study in Canada

Canada is one of the most popular study destinations in the world due to its high focus on the quality of its universities and its emphasis on attracting international students who can later immigrate. Canadians are very welcoming to international students and they invest a lot into making sure students are safe, treated fairly, and enjoy their stay in the country. Study in one of the strongest economies in the world while enjoying a high living standard and a flexible study environment. Classes have smaller student groups ensuring everyone gets the attention they need, and encouraging group assignments and debates.

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Explore your Computer Sciences degree

Computer Sciences is the study of computers, focusing on algorithms, data analysis, automation, and computing theory. Specialisations include Software Engineering, Data Science, AI, Cybersecurity, and Computer Systems and Networks. The program teaches you in depth about computer systems, programming languages, algorithm design, and software development. For that, it requires proficiency in mathematics, research, and analysis. Career options include Software Developer, Data Scientist, Cybersecurity Analyst, Network Architect, and AI Engineer. Whether you will work in software development, data analysis, AI, or cybersecurity, a degree in Computer Sciences is a valuable career choice for tech enthusiasts with math skills.

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• Doctor of Philosophy in Computer Science (PhD)
• Graduate School
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Canadian Immigration Updates

Applicants to Master’s and Doctoral degrees are not affected by the recently announced cap on study permits. Review more details

Go to programs search

PhD students in the Department of Computer Science may focus their research in the following areas:

• Artificial Intelligence:  computer vision, decision theory/game theory, knowledge representation and reasoning, intelligent user interfaces, machine learning, natural language understanding and generation, robotics and haptics.
• Computer Graphics:  animation, imaging, modeling, rendering, visualization.
• Data Management and Mining:  business intelligence, data integration, genomic analysis, text mining, web databases.
• Formal Verification and Analysis of Systems:  analog, digital and hybrid systems, VLSI, protocols, software.
• Human Centered Technologies:  human computer interaction (HCI), visual, haptic and multimodal interfaces, computer-supported cooperative work (CSCW), visual analytics.
• Networks, Systems, and Security:  high performance computing/parallel processing, networking, operating systems and virtualization, security.
• Scientific Computing:  numerical methods and software, differential equations, linear algebra, optimization.
• Software Engineering and Programming Languages:  development tools, foundations of computation, middleware, programming languages, software engineering.
• Theory: algorithm design and analysis (including empirical), algorithmic game theory, discrete optimization, graph theory, computational geometry

For specific program requirements, please refer to the departmental program website

What makes the program unique?

The UBC Department of Computer Science has many contacts in the computing industry. A strong rapport between the industry and research communities is beneficial to both, especially in cases where the department focuses its research to developing real-world applications.

I love Vancouver! It's the greatest city in the world. I love the integration of nature into the city; it has all of the mountains, forests, and oceans. In addition, the city is a melting pot of cultures, and that's definitely reflected at UBC. It feels like there's a place for everyone at UBC.

Michael Yin

Quick Facts

Program enquiries, admission information & requirements, 1) check eligibility, minimum academic requirements.

The Faculty of Graduate and Postdoctoral Studies establishes the minimum admission requirements common to all applicants, usually a minimum overall average in the B+ range (76% at UBC). The graduate program that you are applying to may have additional requirements. Please review the specific requirements for applicants with credentials from institutions in:

• Canada or the United States
• International countries other than the United States

Each program may set higher academic minimum requirements. Please review the program website carefully to understand the program requirements. Meeting the minimum requirements does not guarantee admission as it is a competitive process.

English Language Test

Applicants from a university outside Canada in which English is not the primary language of instruction must provide results of an English language proficiency examination as part of their application. Tests must have been taken within the last 24 months at the time of submission of your application.

Minimum requirements for the two most common English language proficiency tests to apply to this program are listed below:

TOEFL: Test of English as a Foreign Language - internet-based

Overall score requirement : 100

IELTS: International English Language Testing System

Overall score requirement : 7.0

Other Test Scores

Some programs require additional test scores such as the Graduate Record Examination (GRE) or the Graduate Management Test (GMAT). The requirements for this program are:

The GRE is not required.

2) Meet Deadlines

3) prepare application, transcripts.

All applicants have to submit transcripts from all past post-secondary study. Document submission requirements depend on whether your institution of study is within Canada or outside of Canada.

Letters of Reference

A minimum of three references are required for application to graduate programs at UBC. References should be requested from individuals who are prepared to provide a report on your academic ability and qualifications.

Statement of Interest

Many programs require a statement of interest , sometimes called a "statement of intent", "description of research interests" or something similar.

Supervision

Students in research-based programs usually require a faculty member to function as their thesis supervisor. Please follow the instructions provided by each program whether applicants should contact faculty members.

Instructions regarding thesis supervisor contact for Doctor of Philosophy in Computer Science (PhD)

Citizenship verification.

Permanent Residents of Canada must provide a clear photocopy of both sides of the Permanent Resident card.

4) Apply Online

All applicants must complete an online application form and pay the application fee to be considered for admission to UBC.

Tuition & Financial Support

Financial Support

Applicants to UBC have access to a variety of funding options, including merit-based (i.e. based on your academic performance) and need-based (i.e. based on your financial situation) opportunities.

Program Funding Packages

All full-time PhD students will be provided with a funding package of at least $31,920 for each of the first four years of their PhD program. The funding package consists of any combination of internal or external awards, teaching-related work, research assistantships, and graduate academic assistantships. This support is contingent on full-time registration as a UBC Graduate student, satisfactory performance in assigned teaching and research assistantship duties, and good standing with satisfactory progress in your academic performance. CS students are expected to apply for fellowships or scholarship to which they are eligible.

Average Funding

• 40 students received Teaching Assistantships. Average TA funding based on 40 students was $6,950.
• 77 students received Research Assistantships. Average RA funding based on 77 students was $20,513.
• 18 students received Academic Assistantships. Average AA funding based on 18 students was $6,167.
• 81 students received internal awards. Average internal award funding based on 81 students was $11,015.
• 8 students received external awards. Average external award funding based on 8 students was $19,625.

Scholarships & awards (merit-based funding)

All applicants are encouraged to review the awards listing to identify potential opportunities to fund their graduate education. The database lists merit-based scholarships and awards and allows for filtering by various criteria, such as domestic vs. international or degree level.

Graduate Research Assistantships (GRA)

Many professors are able to provide Research Assistantships (GRA) from their research grants to support full-time graduate students studying under their supervision. The duties constitute part of the student's graduate degree requirements. A Graduate Research Assistantship is considered a form of fellowship for a period of graduate study and is therefore not covered by a collective agreement. Stipends vary widely, and are dependent on the field of study and the type of research grant from which the assistantship is being funded.

Graduate Teaching Assistantships (GTA)

Graduate programs may have Teaching Assistantships available for registered full-time graduate students. Full teaching assistantships involve 12 hours work per week in preparation, lecturing, or laboratory instruction although many graduate programs offer partial TA appointments at less than 12 hours per week. Teaching assistantship rates are set by collective bargaining between the University and the Teaching Assistants' Union .

Graduate Academic Assistantships (GAA)

Academic Assistantships are employment opportunities to perform work that is relevant to the university or to an individual faculty member, but not to support the student’s graduate research and thesis. Wages are considered regular earnings and when paid monthly, include vacation pay.

Financial aid (need-based funding)

Canadian and US applicants may qualify for governmental loans to finance their studies. Please review eligibility and types of loans .

All students may be able to access private sector or bank loans.

Foreign government scholarships

Many foreign governments provide support to their citizens in pursuing education abroad. International applicants should check the various governmental resources in their home country, such as the Department of Education, for available scholarships.

Working while studying

The possibility to pursue work to supplement income may depend on the demands the program has on students. It should be carefully weighed if work leads to prolonged program durations or whether work placements can be meaningfully embedded into a program.

International students enrolled as full-time students with a valid study permit can work on campus for unlimited hours and work off-campus for no more than 20 hours a week.

A good starting point to explore student jobs is the UBC Work Learn program or a Co-Op placement .

Tax credits and RRSP withdrawals

Students with taxable income in Canada may be able to claim federal or provincial tax credits.

Canadian residents with RRSP accounts may be able to use the Lifelong Learning Plan (LLP) which allows students to withdraw amounts from their registered retirement savings plan (RRSPs) to finance full-time training or education for themselves or their partner.

Please review Filing taxes in Canada on the student services website for more information.

Cost Estimator

Applicants have access to the cost estimator to develop a financial plan that takes into account various income sources and expenses.

Career Outcomes

111 students graduated between 2005 and 2013. Of these, career information was obtained for 106 alumni (based on research conducted between Feb-May 2016):

Sample Employers in Higher Education

Sample employers outside higher education, sample job titles outside higher education, phd career outcome survey, career options.

Our faculty and students actively interact with industry in numerous fields. Via internships, consulting and the launching of new companies, they contribute to the state-of-the-art in environmental monitoring, energy prediction, software, cloud computing, search engines, social networks, advertising, e-commerce, electronic trading, entertainment games, special effects in movies, robotics, bioinformatics, biomedical engineering, and more.

Alumni on Success

Job Title Senior Director, Product & Business Development

Employer NGRAIN

Enrolment, Duration & Other Stats

These statistics show data for the Doctor of Philosophy in Computer Science (PhD). Data are separated for each degree program combination. You may view data for other degree options in the respective program profile.

ENROLMENT DATA

Completion Rates & Times

• Research Supervisors

Advice and insights from UBC Faculty on reaching out to supervisors

These videos contain some general advice from faculty across UBC on finding and reaching out to a supervisor. They are not program specific.

This list shows faculty members with full supervisory privileges who are affiliated with this program. It is not a comprehensive list of all potential supervisors as faculty from other programs or faculty members without full supervisory privileges can request approvals to supervise graduate students in this program.

• Beschastnikh, Ivan (Computer and information sciences; software engineering; distributed systems; cloud computing; software analysis; Machine Learning)
• Bowman, William (Computer and information sciences; Programming languages and software engineering; Programming languages; Compilers; programming languages)
• Carenini, Giuseppe (Artificial intelligence, user modeling, decision theory, machine learning, social issues in computing, computational linguistics, information visualization)
• Clune, Jeff
• Conati, Cristina (artificial intelligence, human-computer interaction, affective computing, personalized interfaces, intelligent user interfaces, intelligent interface agents, virtual agent, user-adapted interaction, computer-assisted education, educational computer games, computers in education, user-adaptive interaction, Artificial intelligence, adaptive interfaces, cognitive systems, user modelling)
• Condon, Anne (Algorithms; Molecular Programming)
• Ding, Jiarui (Bioinformatics; Basic medicine and life sciences; Computational Biology; Machine Learning; Probabilistic Deep Learning; single-cell genomics; visualization; Cancer biology; Computational Immunology; Food Allergy; neuroscience)
• Evans, William (Computer and information sciences; Algorithms; theoretical computer science; Computer Sciences and Mathematical Tools; computational geometry; graph drawing; program compression)
• Feeley, Michael (Distributed systems, operating systems, workstation and pc clusters)
• Friedlander, Michael (numerical optimization, numerical linear algebra, scientific computing, Scientific computing)
• Friedman, Joel (Computer and information sciences; Algebraic Graph Theory; Combinatorics; Computer Science Theory)
• Garcia, Ronald (Programming languages; programming languages)
• Greenstreet, Mark (Dynamic systems, formal methods, hybrid systems, differential equations)
• Greif, Chen (Numerical computation; Numerical analysis; scientific computing; numerical linear algebra; numerical solution of elliptic partial differential equations)
• Gujarati, Arpan (Computer and information sciences; Systems)
• Harvey, Nicholas (randomized algorithms, combinatorial optimization, graph sparsification, discrepancy theory and learning theory; algorithmic problems arising in computer networking, including cache analysis, load balancing, data replication, peer-to-peer networks, and network coding.)
• Holmes, Reid (Computer and information sciences; computer science; open source software; software comprehension; software development tools; software engineering; software quality; software testing; static analysis)
• Hu, Alan (Computer and information sciences; formal methods; formal verification; model checking; nonce to detect automated mining of profiles; post-silicon validation; security; software analysis)
• Hutchinson, Norman (Computer and information sciences; Computer Systems; distributed systems; File Systems; Virtualization)
• Kiczales, Gregor (MOOCs, Blended Learning, Flexible Learning, University Strategy for Flexible and Blended Learning, Computer Science Education, Programming Languages, Programming languages, aspect-oriented programming, foundations, reflections and meta programming, software design)
• Lakshmanan, Laks (data management and data cleaning; data warehousing and OLAP; data and text mining; analytics on big graphs and news; social networks and media; recommender systems)
• Lecuyer, Mathias (Machine learning systems; Guarantees of robustness, privacy, and security)
• Lemieux, Caroline (Programming languages and software engineering; help developers improve the correctness, security, and performance of software systems; test-input generation; specification mining; program synthesis)
• Leyton-Brown, Kevin (Computer and information sciences; Artificial Intelligence; Algorithms; theoretical computer science; Resource Allocation; Computer Science and Statistics; Auction theory; game theory; Machine Learning)
• MacLean, Karon (Computer and information sciences; Information Systems; design of user interfaces; haptic interfaces; human-computer interaction; human-robot interaction)

Doctoral Citations

Sample Thesis Submissions

• Discrete optimization problems in geometric mesh processing
• On effective learning for multimodal data
• From devices to data and back again : a tale of computationally modelling affective touch
• Towards alleviating human supervision for document-level relation extraction
• Methods for design of efficient on-device natural language processing architectures
• A formal framework for understanding run-time checking errors in gradually typed languages
• Understanding semantics and geometry of scenes
• Computational tools for complex electronic auctions
• From videos to animatable 3d neural characters
• Structured representation learning by controlling generative models
• Versatile neural approaches to more accurate and robust topic segmentation
• Machine learning for spectroscopic data analysis : challenges of limited labelled data
• Enriching block-based end-user programming with visual features
• Accelerating Bayesian inference in probabilistic programming
• Computationally efficient geometric methods for optimization and inference in machine learning

Related Programs

Same specialization.

• Master of Science in Computer Science (MSc)

Same Academic Unit

• Master of Data Science (MDS)

At the UBC Okanagan Campus

Further information, specialization.

Computer Science covers Bayesian statistics and applications, bioinformatics, computational intelligence (computational vision, automated reasoning, multi-agent systems, intelligent interfaces, and machine learning), computer communications, databases, distributed and parallel systems, empirical analysis of algorithms, computer graphics, human-computer interaction, hybrid systems, integrated systems design, networks, network security, networking and multimedia, numerical methods and geometry in computer graphics, operating systems, programming languages, robotics, scientific computation, software engineering, visualization, and theoretical aspects of computer science (computational complexity, computational geometry, analysis of complex graphs, and parallel processing).

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Faculty of Arts and Sciences

PhD in Computer Science

Graduate 3-175-1-0

Bon à savoir! Ce programme s’adresse autant aux francophones qu’aux anglophones. Consultez la fiche descriptive en français pour en savoir plus.

Specialize in an area of computer science research while gaining international expertise in a cutting-edge field.

About this program

Be part of the rapid developments in computer science.

This PhD program will immerse you in the field of research by writing a thesis to drive advances in computer science. By carrying out a research project supervised by a member of the teaching team or a Research Chair, you will have the opportunity to be part of the new generation of researchers specializing in a cutting-edge area of computer science, such as artificial intelligence, machine learning, quantum computing, operations research, software engineering, bioinformatics, and computer graphics, etc.

• Internationally renowned faculty in artificial intelligence, machine learning, quantum computing, operations research, etc.
• Multidisciplinary environment based on a culture of excellence in research and innovation
• Wide range of courses in various areas of computer science
• Internationally renowned research groups and chairs
• Guaranteed funding for all candidates

Language accommodations

Although Université de Montréal is a French-language university, many of our research departments are open to creating a bilingual environment for students in graduate-level programs. These departments are aware of the importance of bilingualism in order for graduate students to succeed in their research careers.

Students who are proficient in English are therefore welcome and accepted into graduate-level programs.

The Department of Computer Science and Operations Research provides a number of accommodations to make you feel comfortable and help you fulfill the requirements of the program even if French is not your main language of study:

• Access to course notes in both French and English.
• Possibility of taking an equivalent course at an English-language university in Montréal.
• Choice of giving oral seminar presentations in either French or English.
• Choice of writing exams in French or English.
• Choice of taking the comprehensive exam in either French or English.
• Choice of writing your doctoral thesis or dissertation in French or English.

All laboratories are offered in a bilingual environment. Above all, staffs in the program are available to help and support you throughout your studies.

Advantages of studying computer science at UdeM Focus on scientific excellence and innovation

• Work in a wide range of research laboratories and groups, conducting cutting-edge work in logistics, artificial intelligence, machine learning, natural language processing, video games, quantum computing, bioinformatics, cryptography, etc.
• Join the multidisciplinary environment of the Department of Computer Science and Operations Research (DIRO), which receives over $6 million in annual research funding
• Financing your studies : all PhD candidates are guaranteed funding

Courses and specifics

For contact information on the program director or student file management technician, or to find out more about the faculty or department hosting the program, please see the page in French . 

• Dissertation or thesis track
• International exchange option

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The opportunity to think outside the box and the freedom of expression at DIRO are among the most important elements of my education at UdeM.

PhD in Computer Science, President and Founder of NLP Technologies

Admission requirements

You are eligible without having confirmed a research supervisor, but you should do so within a prescribed period.

Be sure to select your choices to display the eligibility conditions that apply to you.

• The Supervision Consent Form , including a section on financial arrangements.

Costs and financial aid

$2,117.74 *

Total for a full-time session of 15 credits

Tuition fees: $1,483.65

Other fees: $634.09

These amounts are estimates and do not account for other expenses, such as costs for insurance, residence, transportation, manuals, etc.

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* These estimates cannot at any time subsitute for an invoice or be used as proof for any reason whatsoever. These calculations are based on the 2024-2025 academic year. Information updated: June 5, 2024

Good news! You may be able to lower this amount!

Under certain conditions, Canadian students who are not residents of Quebec can follow university study programs offered in French while benefiting from the same tuition fees as residents of Quebec.

Check eligibility criteria

$9,753.94 *

Tuition fees: $9,119.85

As an international student, you have access to exemption scholarships granted by UdeM throughout your university program. Note that for ungraduated programs, you must be enrolled as a full-time student for two sessions and reside in Quebec in the case of exclusively online study programs.

Find out about the UdeM exemption scholarship

Future prospects Pursuing a career or further studies? The choice is yours!

Benefit from an exceptional placement rate and gain access to the most interesting and best-paying jobs in the information and communications technology sector. Excellent job opportunities are available with private companies, consulting firms, government corporations and financial institutions, among others.

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PhD in Computer Science

The PhD in Computer Science program combines coursework, a Comprehensive I (breadth) exam by which the candidate demonstrates a breadth of knowledge in a broad range of research areas in Computer Science, a Comprehensive II exam by which the candidate demonstrates a depth of knowledge in the chosen research area, and seminars, leading to a thesis. 

Note: The School of Computer Science does not accept part-time students into the PhD program unless the applicant is currently an employee of the School.

PhD course requirements

The following is a brief outline of the PhD course requirements. 

PhD from master's:

• 4 one-term graduate courses
• at least 3 of the courses must be above the 600-level
• a minimum of one 800-level course
• any required remedial courses

PhD from bachelor's

• 8 one-term graduate courses
• at least 5 of the courses must be above the 600-level
• a minimum of three 800-level courses

Students are permitted to carry over any extra courses from their MMath program at Waterloo. Note the courses must be noted as extra on the transcript.

PhD comprehensive requirements

Phd comprehensive-i (breadth).

The PhD Comprehensive-I (Breadth) requirement ensures that a student has sufficient breadth of knowledge to undertake research at the PhD level. A student meets the requirement by taking a number of advanced courses in a broad range of categories and areas of Computer Science. 

• The 6 courses used must all have a minimum mark of 78% (B+ or equivalent).
• Breadth courses must cover each of 3 broad categories in Computer Science (Computing Technology, Mathematics of Computing, and Applications). See Table 1 .
• 6 of the 11 areas shown in Table 1 must be covered by breadth courses.
• Courses for the breadth requirement can include 4th year (400+ level) advanced undergraduate courses, courses taken at master's level and completed or proposed courses at doctoral level.

Table 1: Categories and Areas for Breadth Requirement

See more details

PhD Comprehensive-II (Depth)

As of Fall 2019, new  University regulations  regarding comprehensive exams will come into effect. Graduate students within the Faculty of Mathematics should familiarize themselves with these regulations. In particular, based on these new regulations students are expected to complete their Comprehensive exam by the end of Term 7. 

Additional information about Comprehensive exams can be found here .

• an oral exam normally taken within 7 terms of entering the PhD program which tests the student's preparedness to pursue thesis research
• requires an oral presentation of a Research Proposal (not a Thesis Proposal) together with questioning by the Advisory Committee made up of the supervisor (and co-supervisor) and 2 additional faculty members from the School of Computer Science
• the candidate must convince the committee that the chosen research area is suitable and demonstrate an appropriate breadth of knowledge in the chosen area
• the committee must determine if there is a thesis topic in the area and whether the candidate is capable of completing such a thesis

PhD seminar requirement

• requires the presentation of 3 publicly-posted seminars (or lectures, possibly in 700 level courses) in the School of Computer Science
• the purpose of this requirement is twofold: first, it ensures that each student participates in the academic life of the School and, second, it provides an opportunity for students to hone their presentational skills.

PhD Internship (if applicable):

• Describe a topic that is relevant and that is not deemed confidential by your employer
• Contain constructive criticism, conclusions and/or recommendations
• Compare and evaluate several items or alternatives using various criteria
• Discuss the underlying problem, your approach to solving the problem, and the progress you made

If the department is not satisfied with the quality of your report you will be expected to re-write it and resubmit within the required time frame.

The PhD thesis oral examination culminates the candidate's research program. It exposes the candidate's work to scholarly criticism by members of the University, and gives the student the opportunity to defend it.

For regulations and guidelines see more details .

Have questions? Please visit our FAQ page .

PhD milestones: request for public posting 

PhD Seminar

PhD Defense

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Dalhousie University

Computer science  mcsc, phd.

• Faculty of Computer Science

Program details

• Funding & support
• Graduate life
• PhD program
• Master's program
• Graduate support & resources

Important dates

• October 15:  Applications for September 2023 admissions open
• December 1:  NSERC CGS-M  
• January 16:   Harmonized scholarship  applications due 
• April 1:  International applications for September 2023 admissions close 
• June 1:  Domestic applications for September 2023 admissions close

Funded Fellowships

We offer competitive funding to qualified graduate students, including a variety of funded fellowship opportunities  available to incoming PhD students. 

Make an impact through research

You can conduct original and independent research alongside our  award-winning professors  in areas that cut across many industries and permeate nearly all human endeavors. From oceans to healthcare, information communications technology to gaming, our students and professors are  making an impact . The Faculty of Computer Science at Dalhousie University is the  premier academic research institution in Information Technology in Atlantic Canada.  Since our founding in 1997, the Faculty has developed research strengths across five major areas:  Big Data Analytics, Artificial Intelligence & Machine Learning ,  Human-Computer Interaction, Visualization & Graphics ,  Systems ,  Algorithms & Bioinformatics , and  Computer Science Education .

Designed with support in mind 

The PhD program is designed to focus you on research early, under the mentorship of a thesis supervisor, assisted by a supervisory committee. Students are expected to lead a well-defined component of a wider project, and be the prime author in the resulting publications.

In addition to exploring theories, methodological concepts, and substantive knowledge related to computer science, our PhD in Computer Science will help you develop a deep expertise in areas like: 

• Visual Analytics
• Text and Data Analytics
• Networks and Security
• Wireless networks and Security
• Human-Computer Interaction and Ubiquitous Computing
• Visualization, New Media and Image Processing
• Mobile Graphics
• Theory and Algorithms
• Genetic Algorithms and Evolutionary Computing
• Bioinformatics
• Health Informatics
• Autonomous Robotics
• Algorithms & Data Structures
• CS Education
• Creative AI
• Educational Data Mining & Learning Analytics
• Human-Centred Design
• Software Engineering

Your future

By the time you've completed your degree, you will be ready for a career in industry, or within an academic setting. Some of our alumni have found jobs as:

• Professors in computer science 
• Conducting advanced research in industrial or government research labs
• Chief Technical Officers
• CEO of their own start-up

PhD in Computer Science

The PhD program requirements consist of a number of graduate courses, a Research Aptitude Defence , a Thesis Proposal, and the Thesis Defence . The general regulations of the Faculty of Graduate Studies are in effect for the PhD program. Here detailed information is provided about the PhD Program requirements in the Faculty of Computer Science.

Program structure

A PhD in Computer Science will typically take 3 - 4 years to complete. 

Learn more about timelines for satisfactory progress in this program.

Admission requirements

PhD requirements follow the standard requirements by the Faculty of Graduate Studies.

Learn about admission requirements / how to apply .

Degree requirements

The PhD course requirement for a candidate entering the PhD program with a Master's degree in Computer Science is TWO graduate courses (CSCI 6XXX or above), plus any additional graduate or undergraduate courses mentioned in the letter of admission. Up to one CSCI 6902 doctoral directed studies course can be taken as one of the two required graduate courses. The PhD course requirement for a candidate entering the PhD program with a Bachelor of Computer Science degree is SIX graduate courses (CSCI 6XXX or above), plus any additional graduate or undergraduate courses mentioned in the letter of admission. Up to one CSCI 6901 directed studies course and one CSCI 6902 doctoral directed studies course can be taken as two of the six required courses. All PhD candidates should consult with and get the agreement of their supervisor on their graduate course selection.

Sufficient seminar attendance done to receive a passing grade in CSCI 6999 (attend the lesser of 6 or 75% of seminars denoted for 6999 in the term for each of 6 terms).

In addition to the above graduate coursework requirement, a candidate is required to register in a Directed Doctoral Research Project, CSCI 7900.06 (worth one full course or 6 credit hours), leading to the research aptitude exam. Students should sign up for it in the two consecutive terms before their exam. The course reflects the effort that students put into their research leading to that exam.

Additional courses may be required either as specified upon admission or as a result of the student's performance in the research aptitude/thesis proposal examination to remedy deficiencies in the student's background.

On successful defense of the research aptitude exam candidates enroll in CSCI 9530 for the remainder of their program.

Choosing a research supervisor

Before you begin, you'll need to find a supervisor who is able to support your research interests and has the capacity to take on the added responsibility. This individual will be an important part of your supervisory committee. Start by checking out  our faculty profiles  and  faculty research interests , then get in touch with them directly.

Research Aptitude Defence

The research aptitude defence is an internal review of a directed research project to date. The objective of this review is to assess the likelihood of the work yielding thesis quality material in the near, medium and long term. Candidates may provide strong evidence in support of the above objectives by having directed research accepted for publication in a peer reviewed conference / journal. The preparation of the student for this defence is part of the normal research process towards the PhD, and therefore it does not detract from, but contributes to the objective of timely completion of the degree.

Important points:

• Candidates are expected to take and pass the RAD within 5 terms of beginning their program;
• If candidates fail to pass the RAD within this timeline, Faculty scholarship funding will be cut off;
• Decisions regarding any Extensions/ Restoration of funding are made by the Graduate Committee on a case-by-case basis.

See here for detailed information about the Research Aptitude Defence & Thesis Defence Proposal.  It is the responsibility of the student to provide committee members with a copy of the report ten working days in advance of the defence.

Administrative forms for scheduling and reporting the result of the research aptitude defence can be found on the  graduate forms page .

When designing your thesis, you should keep in mind that to be successful, it must describe an original contribution to knowledge made while you attended Dalhousie University. It also needs to be valuable enough to merit publication in a reputable scientific journal with a system of external review.  

Research for the thesis is conducted under the guidance of your research supervisor, in whose laboratory you work. A Thesis Supervisory Committee for each student provides additional expertise and advice to facilitate the research and the preparation of the thesis.

Detailed information about the Thesis Proposal.  It is the responsibility of the student to provide committee members with a copy of the report ten working days in advance of the defense.

Administrative forms for scheduling and reporting the result of the thesis proposal defence can be found on the  graduate forms page .

Candidates are expected to take and pass the Thesis Proposal defence within 8 terms of beginning their program

Important application deadlines

June 1  (for September start) is the deadline for Canadian applicants.

April 1  (for September start) is the deadline for non-Canadian applicants. If you think your visa processing will take some time, please apply by March 1.

Contact information

If you have more questions, contact:

[email protected]

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Université de Montréal / Faculty of Arts and Science Department of Computer Science and Operations Research

• Graduate programs

PhD in Computer Science

Do you have a passion for research? Would you like to directly influence the state of the art in advanced computer science? Do you want to work with world-renowned experts in academia and industry on the important problems that will shape the future? Then the  PhD in Computer Science  is the ideal program for you!

Most of this program is devoted to advancing knowledge in this field, by conducting research and devising new solutions to important open problems

• Fall, winter and summer admission
• Daytime classes
• Full-time or part-time
• 90 credits, including 84 for research and thesis writing

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The program culminates in a thesis that opens up new avenues of research while solving an important set of problems in your chosen area of expertise.

Prior to the thesis-writing stage, PhD candidates will be evaluated on their general knowledge in computer science and, at a later stage of their training, the candidates' knowledge of their chosen area of specialization will also be evaluated. The final evaluation for a PhD involves the defence of the candidate's work.

For Mila programs (DESS in Machine Learning, Professional MSc in Machine Learning and Research MSc and PhD in machine learning), the application procedure is in two steps (please read the document Procedure for applying to MILA programs )

ONCE ADMITTED, PLEASE COMPLETE AN OFF-CAMPUS STUDY AUTHORIZATION (AEHE) IN ORDER TO TAKE COURSES OUTSIDE THE UNIVERSITY OF MONTREAL.

Questions about this program?

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All about this program (in French)

Important forms, readmission, find a thesis supervisor, plan global d'études, prolongation, international, financial assistance, job outlook for graduates in this discipline, comprehensive examination (pre-doc), useful links, the advantages of studying computer science at the udem.

• The chance to work with world-renowned professors in a wide range of  exciting research laboratories and groups , including cutting-edge work in video games and computer graphics, artificial intelligence, computation linguistics and quantum computing, just to name a few.
• The Université de Montréal ranks 29 th  in the world in operations research according to the  QS Rankings  and 97 st  in the world in computer science according to the  NTU Rankings .

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PhD Program

The student must complete the following requirements for the PhD program:

• Pass the Research Proficiency Evaluation (RPE)
• Complete the Comprehensive Course Requirement
• Successfully defend the Thesis Proposal Exam
• Pass the Final Doctoral Examination
• Have the final thesis approved by Faculty of Graduate and Post Doctoral Studies

Completing the first two milestones fulfills the Faculty of Graduate and Post Doctoral Studies Comprehensive exam requirement. Completing the first three milestones enables the student to be admitted to candidacy. 

A student is expected to be admitted to candidacy within 36 months from the date of initial registration and to complete the PhD within 6 years. Extensions to these deadlines maybe granted under exceptional circumstances with the permission of the Graduate Affairs Committee and Faculty of Graduate and Postdoctoral Studies.

Research Supervisor and Supervisory Committee

Every incoming graduate student is assigned a faculty member as his/her advisor.  These assignments are made keeping in mind the research interests of the student and the workload of the faculty member. In the case of entering PhD students, the advisor is a member of the “offer sponsorship team,” which consists of one to three faculty. It is the student’s responsibility to formalize a supervisory relationship with a faculty member in his or her area of interest within one term. The advisor and any other sponsors are natural choices for this role, but other faculty may also be considered (if they are interested). Please refer to PhD Supervisory Committee for details.

Research Proficiency Evaluation (RPE)

Students admitted to the PhD program are required to demonstrate their research proficiency by completing a research project with under the supervision of a one or more faculty members, and presenting their results in a written report and oral examination before their RPE committee.  Please refer to RPE for details.

Comprehensive Course Requirement

All PhD students are required to submit the Comprehensive Course Requirement form   to the Graduate Affairs Committee within the first two months of the initial registration.  The objective of the comprehensive course requirement is to ensure that the student obtains a breadth of knowledge of computer science, as well as sufficient depth in a specific field.  Students should indicate what courses they will be taking or have taken that can satisfy the breadth and depth components of the comprehensive course requirement.  If the student has taken courses outside the department that can satisfy the breadth requirement, s/he must contact the faculty in the research area for approval.  Once the comprehensive course proposal is approved, the student can take the courses in accordance with the proposal.

Note that for courses not contributing to the comprehensive course requirement, a minimum of 68% (B-) must be achieved. Courses contributing to the comprehensive course requirement have a even higher requirement. Please refer to PhD Program Comprehensive Course Requirement for details.

Thesis Proposal

Having formalized a thesis supervisor and having successfully completed the RPE, the student will continue with the development of a PhD thesis proposal.  This proposal must be presented in written form to the supervisory committee by the end of the second year of the PhD program. Please refer to Thesis Proposal for details.

Completing the Research Program

Once the thesis proposal examination is passed, the student must carry out a research program in accordance with his or her research proposal under the supervisor’s guidance, with periodic reviews by the student’s committee. A thesis describing his or her research findings must be written by the student, approved by the committee and an external examiner, and defended at a final oral examination set up by the Faculty of Graduate and Postdoctoral Studies. A guide to the preparation of PhD theses is provided by the Faculty of Graduate and Postdoctoral Studies. The student has the final responsibility for meeting the requirements and deadlines of the Faculty of Graduate and Postdoctoral Studies.

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Computer Science (PhD)

Program overview Program structure Admission requirements Application process Tuition & funding

Program overview

The PhD in Computer Science program leads to the highest degree offered by the Faculty and is designed to provide students an opportunity to obtain the greatest possible expertise in their chosen field through intensive research. Advancement of analytical and/or experimental knowledge through a combination of specialized courses and a research thesis under the supervision of an experienced researcher forms the main component of the doctoral program. Where possible, research of interest to industry is encouraged.

The objective of the PhD in Computer Science program is to educate highly qualified researchers required for the expansion of fundamental knowledge and technological innovation through research and development, as well as the needs of institutions of higher learning.

Program structure

Degree requirements, (90 credits), doctor of/doctorate in philosophy (phd).

Admission requirements

Admission requirements.

Admission on a full-time basis

• Master’s degree or equivalent with high standing in engineering or computer science, or in a cognate discipline.
• Holders of a bachelor’s degree will, in general, be considered for admission to a master’s program only. After completion of a minimum of one term of full-time study in the Master's degree, they may, upon application, be recommended by the Department and approved by the GCS Associate Dean of Research and Graduate Studies for admission to a PhD program.

Admission on a part-time basis

• Master’s degree with high standing in engineering, computer science or a cognate discipline.

Proficiency in English

Proficiency in English. Applicants whose primary language is not English must demonstrate that their knowledge of English is sufficient to pursue graduate studies in their chosen field. Please refer to the English language proficiency page for further information on requirements and exemptions .

Application process

Application deadlines.

All applicants: Canadian / International / Permanent Resident

June 1 (all applicants)

October 1 (all applicants)

February 1 (all applicants)

Priority will be given to complete applications submitted by the deadline. In some cases, programs may continue to accept applications as long as there is space available.

International students: Considering the waiting period involved in meeting the entry requirements to Canada and Quebec , we strongly encourage international applicants to apply early and submit supporting documents prior to the deadline.

Tuition & funding

Tuition and fees.

Tuition and fees of the program may depend on your student status, among other key factors. Estimate these costs based on the most common situations.

Awards and funding

Funding packages are generally available for students in thesis-based programs. They come in the form of awards, teaching and research assistantships are offered at the time of admission to most students to allow them to focus on their research and studies. Research and thesis-based students are automatically considered for all entrance graduate awards when they apply to Concordia, provided they meet eligibility criteria. No separate application is required.

The Quebec and Canadian governments offer a number of competitive graduate scholarships. We encourage you to apply for these awards at the same time you are preparing your application.

Other programs of interest

Software engineering (phd) thesis.

Deepen your understanding of sophisticated engineering methodologies through intensive research and the application of mathematical, computer science and software engineering concepts.

Department of Computer Science and Software Engineering

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Computing science.

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Degree Requirements

Doctor of philosophy.

The Doctor of Philosophy (PhD) in Computing Science is a research-intensive program that has a primary emphasis on the thesis. The Program provides an environment for interdisciplinary education in theoretical and applied Computer Science. Through training in formal coursework and hands-on research in areas such as artificial intelligence, computer systems and networks, computer graphics, and data mining, graduates will be capable of working with integrity to design, improve, and apply cutting-edge computational techniques to support a career in academia, industry or the public sector.

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Computer Science (Doctoral program)

Program details.

Faculty Science

Degree Doctor of Philosophy (PhD)

Delivery method Hybrid online/in-class

Location Ontario Tech University, North Oshawa

Start dates September, January or May

Length Approximately 48 months, based on full-time study

Program load Full-time

Program format Courses with PhD thesis

The goal of the  PhD  program is to produce a new breed of computer science graduates that have a broad background in information technology along with project management and people skills. Graduates of this program will not only have strong technical expertise in their particular  field,  but will also have the ability to work effectively in interdisciplinary teams and be able to tackle problems that require both technical and non-technical solutions.

The program differs from most existing computer science programs as it concentrates on both applied research and the development of professional skills. The intention is that most of the graduates from this program will build careers in industrial research and software development. The program also prepares graduates for careers in academia, but it is expected that most of the graduates from this program will select careers in industry. The program focuses on the skills required for successful careers in industry, reflecting the university's goals to be market-oriented and to provide high-quality professional education.

The  PhD  program gives students the opportunity to work in teams, develop leadership skills, and master oral and written communication skills.

• Digital Media Use of computer technology in the implementation of various forms of media including audio, graphics, computer animation, visual analytics, computer games and computer vision.
• Information Science Distribution and management of information including database systems, machine learning, services computing, intelligent systems and health informatics.
• Networks and IT Security Design, implementation and management of computer networks, as well as security issues such as cryptography, malware analysis and secure communications.
• Software Design Process of designing and implementing software systems, including software engineering, distributed computing, programming languages and software architecture. 
• Admission requirements
• Application deadlines
• How to apply
• Completion of a master's level degree in computer science, computer engineering, information technology or software engineering from a Canadian university, or its equivalent from a recognized institution.
• A minimum B+ average (GPA: 3.3 on a 4.3 scale or 77 to 79 per cent).

Required supporting documents:

Please see the  checklist of required documents  for a list of supporting documentation that must be submitted with your application.

Additional requirements:

Admission depends on the availability of a research supervisor. It is recommended that applicants contact a potential supervisor before formally applying. 

In their statement of academic intent, applicants should include the type(s) of course(s) they feel they are suitable to teach as teaching assistants.

Required test scores for English language proficiency:

See  English language proficiency  for the minimum required test scores for this program.

Please see   application deadlines   for specific dates. Note that the application deadlines listed are for both the online application and all supporting documentation.

Applications for admission to all graduate studies programs are submitted online. There are five steps you must go through to complete the application process. See   application process and requirements   for step-by-step instructions.

Many of our graduate programs are extremely competitive; the number of qualified applicants normally exceeds the number of seats available for each intake. Satisfaction of minimum entry requirements does not ensure admission.

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Research areas

• Artificial intelligence
• Computer graphics
• Computer vision
• Data science
• Distributed computing
• Health informatics
• Information visualization
• Network design
• Network security
• Serious games
• Software engineering
• Ubiquitous computing
• Virtual reality

Learn more about the research areas within this program and find research experts by visiting the faculty’s website and the university's Expert Centre .

Additional information

Internal awards and funding.

Applicants to research-based graduate programs who are studying full-time are automatically considered for some types of funding at the time of admission.

Types of funding that do not require an application:

• Entrance scholarships
• Minimum funding packages
• Teaching assistantships, research assistantships and graduate research assistantships

For more details on the above funding opportunities, see   graduate student awards and funding .

Please note:   Part-time students are not eligible for the above funding opportunities.

External awards and funding

Graduate program applicants are encouraged to apply for   external awards   to help finance their education. The application process differs for each competition, so review the information carefully to determine where and when you must apply.   Please note:   The majority of these awards are for domestic or permanent residents only.

Tuition fees for graduate programs are charged on a flat-fee or fee-per-credit basis and vary by program and student status.

For current, specific fees and details on flat-fee versus fee-per-credit programs, please see   tuition and fees .

Contact the program:

Faculty of Science 905.721.3050 [email protected]

Contact the School of Graduate and Postdoctoral Studies:

905.721.8668 ext. 6209 [email protected]

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University of Manitoba

University of Manitoba Winnipeg, Manitoba Canada, R3T 2N2

Computer Science (PhD)

The Computer Science (PhD) program is designed to provide students an opportunity to obtain the greatest possible expertise in their chosen field through intensive research. 

Program details

Admission requirements.

• Faculty of Science • Faculty of Graduate Studies

• Doctor of Philosophy

Expected duration

Study with us.

Established in 1970, the Department of Computer Science saw rapid expansion in its first ten years as Computer Science grew from a niche interest to an independent, recognized discipline. Today, we are an internationally recognized research centre with opportunities to work in active research laboratories in robotics, bioinformatics and novel interface design, using mathematics and methods from nature to solve computing problems and more.

Explore the research in Computer Science

Participate in cutting-edge research

Robots as co-workers? The Department of Computer Science has a number of ongoing research projects. An example of this, Dr. James Young explores how companies can introduce robots to our work environments and how they can be better integrated into our homes.

Learn more about Dr. Young’s research .

Benefit from areas of specialization

The Department of Computer Science offers seven different areas of specialization. Chose the right fit for you:

• Autonomous agents
• Bioinformatics
• Computational finance
• Data security and privacy lab
• Database and data mining
• Geometric, approximation and distributed algorithms
• Human-Computer Interaction (HCI)
• InterDisciplinary Evolving Algorithmic Sciences (IDEAS)

Discover our scholarships, awards and other financial supports

The Department of Computer Science offers numerous competitive funding opportunities to graduate students, including the University of Manitoba Graduate Fellowship (UMGF) .

Learn more about our  awards and funding .

The Faculty of Graduate Studies and the Faculty of Science offer a four-year program of study leading to a Doctor of Philosophy in Computer Science.

Expected duration: 4 years

Tuition and fees:  Tuition fees are charged for terms one and two and terms four and five. A continuing fee is paid for term three, term six and each subsequent term. (Refer to Graduate tuition and fees .)

In addition to the minimum course requirements of the Faculty of Graduate Studies found in the Graduate Studies Regulations Section , students must complete:

• A minimum of 12 credit hours of coursework at the 7000-level

Sample course offerings

• COMP 7860: Security and Privacy
• COMP 7890: Data-Drive Software Engineering
• COMP 7920: Advanced HCI
• COMP 7926: Computational Finance
• COMP 7950: Advanced Machine Learning

For a full list of courses and descriptions of each, please visit the Academic Calendar .

The following are minimum requirements to be considered for entry into the Computer Science (PhD) program. Meeting these requirements does not guarantee acceptance into the program.

To be considered for admission to the Computer Science (PhD) program, you must have:

• A master's degree or equivalent from a recognized university
• A cumulative GPA of 3.0 or equivalent in the last two years of study

In addition to the admission requirements described here, all applicants must meet the minimum admission and English language proficiency requirements of the Faculty of Graduate Studies .

How to apply

The Computer Science (PhD) program accepts applications for Fall and Winter entry. Applications must be completed online and include several parts:

• $100 application fee (non-refundable)
• Unofficial copies of transcripts and degree certificates
• Research statement
• Supervisor support letter (must have secured supervisor support before applying; supervisor support letter will be requested within the application as a recommendation)
• Two letters of recommendation (must be requested from within the application)
• Proof of English language proficiency , if required

Please read the Faculty of Graduate Studies online application instructions before beginning your application.

Choose a supervisor before you apply

Computer Science MSc and PhD programs are research-intensive and because of this, applicants require a letter of support from a faculty member who is willing to act as their supervisor should they be accepted for admission. They should contact faculty before applying to inquire about the possibility of supervision. See Applying for graduate studies for full details.

Applications are reviewed on a committee basis . The Admissions committee for Architecture reviews applications in March.

Applications open up to 18 months prior to start term.

Applicants must submit their online application with supporting documentation and application fee by the deadline date indicated.

Start or continue your application

Applications are reviewed on a committee basis . The Admissions committee for City Planning reviews applications in March.

Winter applications are accepted on a case-by-case basis.

Applications are reviewed on a committee basis . The Admissions committee for Design and Planning reviews applications in March.

Applications are reviewed on a committee basis . The Admissions committee for Interior Design reviews applications in March.

Applications are reviewed on a committee basis . The Admissions committee for Landscape Architecture reviews applications in March.

Applications are reviewed on a committee basis . The Admissions committee for Anthropology reviews applications in March/April.

Applications are reviewed on a  committee basis . Please contact the department for admission committee review timelines.

Applications open September 1 of year prior to start term.

Application deadlines

Applications open   up to 18 months prior  to start term.

Applications are reviewed on a  committee basis . The Admissions committee for History reviews applications in February.

Applications are reviewed on a  rolling basis .

Applications open July 1 of year prior to start term.

Applications are reviewed on a  Committee basis . The Committee for German and Slavic Studies reviews applications in February/March.

Applications are reviewed on a rolling basis .

Applications are reviewed on a committee basis . The Admissions committee for Management reviews applications in February / March.

Applications are reviewed on a committee basis . The Admissions committee for Physical Therapy reviews applications in April / May.

Applications open  August 1 of the year prior to start term.

Applications are reviewed on a committee basis . Please contact the department for admission committee review timelines.

Applications are reviewed on a committee basis . The Admissions committee for Orthodontics reviews applications in August/September and holds interviews in September/October.

Program currently undergoing review, applications will not be opening at this time.

Select Preventive Dental Science in the Program drop-down on the application form.

Applications are reviewed on a  committee basis . The Admissions committee for Educational Administration, Foundations and Psychology reviews applications in March / April.

Applications to Educational Administration, Foundations and Psychology are currently closed.

Applications are reviewed on a  committee basis . The Admissions committee for Education reviews applications in February / March.

Applications to Education PhD are currently closed.

Applications are reviewed after the deadline, with decisions issued in March - April.

Currently not accepting applications to this program.

Applications are reviewed on a  committee basis . Please contact the department for admission committee review timelines.

Applicants must submit their online application with supporting documentation and application fee by the deadline date indicated. Applications received by the March 1 deadline for a September start-date will receive first consideration for any available funding. Late applications will be considered on a case-by-case basis for any available funding, please contact the department for further information.

Applications are reviewed on a  committee basis . The Admissions committee for Human Rights reviews applications in January - March.

Applications are reviewed on a  committee basis . The Admissions committee for Law reviews applications in January - March.

Applications are reviewed on a  committee basis . The Admissions committee for Nursing (MN) reviews applications in April / May.

Applications are reviewed on a  committee basis . The Admissions committee for Nursing PhD reviews applications in February / March.

Applications are reviewed on a  committee basis . The Admissions committee reviews applications as per the timelines noted below each table.

Winter applications reviewed in October Summer applications reviewed in February Fall applications reviewed in June

Winter applications reviewed in June Summer applications reviewed in October Fall applications reviewed in February

Applicants must submit their online application with supporting documentation and application fee by the deadline date indicated. This includes having the support of a faculty supervisor before you apply.

Applications are reviewed on a  committee basis . The Admissions committee for Natural Resources Management reviews applications in March - June.

After the annual application deadline (see below), applications are reviewed on a committee basis by the Faculty of Social Work internal admissions committee. Once this process is complete, decisions are sent to all applicants in March / April. 

Applications open  July 1 of year prior to start term.

Applications are reviewed on a  committee basis . The Admissions committee for Social Work reviews applications in March / April.

Applications are reviewed on a  committee basis . The Admissions committee for Music reviews Fall term applications in December / January, and Winter term applications in July.

Applications are reviewed on a committee basis . The Admissions committee for Occupational Therapy reviews applications in May / June.

Master of Occupational Therapy regular program  applications open September 15 of the year prior to deadline .

Master of Occupational Therapy accelerated program  applications open October 1  of the year prior to deadline .

The name of your confirmed supervisor is required at the time of application. To identify a prospective thesis research supervisor on your application, please  contact Immunology Faculty members .

Applications are reviewed on a  committee basis . The Admissions committee for Community Health Sciences reviews applications in March / April.

Fall 2025 applications are currently closed.

The name of your preferred supervisor is required at time of application.

Applications are reviewed on a  committee basis . Students selected for in-person interview will be notified in February.

Applications are reviewed on a  committee basis . The Admissions committee for Physician Assistant Studies reviews applications in April.

Offers of admission will be released to successful applicants on May 17, 2024 from the University of Manitoba Master of Physician Assistant Studies, the same day as the University of Toronto BScPA Program and McMaster University Physician Assistant Education Program. The three institutions are pleased to provide applicants their offers on the same day to help with the decision-making process.

Applications are reviewed on a  committee basis . The Admissions committee for Pharmacology and Therapeutics reviews applications one month after the application deadline.

Applications for Pathology MSc are reviewed on a  rolling basis .

Applications for Pathologist Assistant are reviewed on a  committee basis . The Admissions committee for Pathologist Assistant reviews applications in April / May. 

The Pathologist Assistant program only admits Canadian and US students every two years. The Fall 2023 intake has been suspended. The next intake is tentatively scheduled for Fall 2025.

Applications are reviewed on a  committee basis . The Admissions committee for Statistics reviews applications in March / April.

Applications are reviewed on a  committee basis . The Admissions committee for Biological Sciences reviews applications one month after deadline.

Applications are reviewed on a committee basis . The Admissions committee for Indigenous Studies reviews applications in February and June.

Applicants must submit their online application with supporting documentation and application fee by the deadline date indicated. For those who wish to be considered for scholarships, applications must be received by January 15 of the year in which you're seeking admission.

Applications are reviewed on a committee basis . The Admissions Committee for Applied Human Nutrition reviews applications in February.

Les demandes d’admission sont évaluées par un comité . Le comité d’admission évalu les demandes durant les mois de Mars et Avril.  

Les demandes peut être surmise jusqu’à concurrence de 18 mois avant le début de premier trimestre.

Toute demande d’admission en ligne doit être déposée, avec documents à l’appui, au plus tard aux dates indiquées.

Soumettre ou continuer votre application

Department of Computer Science

Our department has as an active, internationally-recognized research program with many faculty members being leaders in their respective fields.

Financial aid and awards

There are a variety of awards and funding options available to help you pay for graduate studies at UM.

Tuition and fees

Learn about the tuition and fee requirements associated with graduate studies at UM.

Explore program requirements and detailed descriptions of required and elective courses offered in the Computer Science (PhD) program.

Explore the Faculty of Graduate Studies

Discovery happens here. Join the graduate students and researchers who come here from every corner of the world. They are drawn to the University of Manitoba because it offers the opportunity to do transformational research.

• Funding, awards and financial aid
• Graduate student experience

Explore the Faculty of Science

Every day scientific discovery impacts us in new and exciting ways, unveiling unimagined wonders of nature and helping us live better lives through innovative solutions. The Faculty of Science aims to share that wonder and impact, producing research scientists who will help shape a better future for us all.

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• Student experience
• Community and partners
• Science Co-op
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Keep exploring

Discover more programs

With over 140 graduate programs across multiple faculties, schools and colleges, the University of Manitoba offers more learning, teaching and research opportunities than any other post-secondary institution in the province.

• Mathematics (PhD)
• Physics (PhD)
• Statistics (PhD)

Join students from around the world in a diverse and supportive community.

What it's like to be a UM undergraduate

Be adventurous, challenge yourself and make a difference.

Opportunities for Indigenous students

Experience a world-class education in the heart of Canada

Why international students study with us

We offer state-of-the-art facilities with 140 years of history.

Admission and application inquiries

Faculty of Graduate Studies Room 500 UMSU University Centre 65 Chancellors Circle University of Manitoba (Fort Garry campus) Winnipeg, MB R3T 2N2 Canada

[email protected]  

Phone: 204-474-9377

Monday to Friday, 8:30 a.m. to 4:30 p.m.

Program inquiries

Department of Computer Science E2-445 EITC, 75 Chancellors Cir University of Manitoba Winnipeg, Manitoba, R3T 5V6 Canada

[email protected] Phone:  204-474-8313

Programs and courses

• Future students
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Doctorate in Philosophy Computer Science

• Degree offered: Doctorate in Philosophy (PhD)
• Registration status options: Full-time
• Language of instruction: English
• within four years
• Academic units: Faculty of Engineering , School of Electrical Engineering and Computer Science , Ottawa-Carleton Institute for Computer Science (OCICS).

Program Description

Students who wish to pursue studies in computer science leading to the degree of Master of Computer Science (MCS) or Doctor of Philosophy in Computer Science (PhD) can do so in joint programs offered by the School of Electrical Engineering and Computer Science (EECS) at the University of Ottawa and the School of Computer Science at Carleton University under the auspices of the Ottawa-Carleton Institute for Computer Science (OCICS).

The Institute is responsible for supervising these programs and for providing a framework for interaction between the universities in graduate computer science education. In addition to the faculty members from the two computer science programs, the Institute also has members with computer science expertise from other departments.

Other Programs Offered Within the Same Discipline or in a Related Area

• Master of Computer Science (MCS)
• Master of Computer Science Specialization in Bioinformatics (MCS)

Fees and Funding

• Program fees:

The estimated amount for university fees associated with this program are available under the section Finance your studies .

International students enrolled in a French-language program of study may be eligible for a differential tuition fee exemption .

• To learn about possibilities for financing your graduate studies, consult the Awards and financial support section.
• Programs are governed by the general regulations in effect for graduate studies and the regulations in effect at Carleton University.
• In accordance with the University of Ottawa regulation, students have the right to complete their assignments, examinations, research papers, and theses in French or in English. In addition, research activities can be conducted in either English or French or both depending on the language used by the professor and the members of the research group.
• Students may include courses from both universities in their programs, and may select a supervisor from either university, but they should apply to the university with which their supervisor is associated. Their study program is administered by the university at which they are enrolled and is subject to its regulations.

Program Contact Information

Graduate Studies Office, Faculty of Engineering STE 1024 800 King Edward Ave. Ottawa ON Canada K1N 6N5

Tel.: 613-562-5347 Fax.: 613-562-5129 Email: [email protected]

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For the most accurate and up to date information on application deadlines, language tests and other admission requirements, please visit the  specific requirements  webpage.

To be eligible, candidates must:

• Have a master's degree in Computer Science (with thesis or equivalent in terms of scholarly publications) with a minimum average of B+ (75%).

Note: International candidates must check the admission equivalencies for the diploma they received in their country of origin.

• Identify at least one professor who is willing to supervise your research and thesis. We recommend that you contact potential thesis supervisors as soon as possible.

Language Requirements

Applicants must be able to understand and fluently speak the language of instruction (French or English) in the program to which they are applying. Proof of linguistic proficiency may be required.

Applicants whose first language is neither French nor English must provide proof of proficiency in the language of instruction.

Note: Candidates are responsible for any fees associated with the language tests.

• The admission requirements listed above are minimum requirements and do not guarantee admission to the program.
• Admissions are governed by the general regulations in effect for graduate studies.

Fast-Track from the Master’s to the PhD

Students enrolled in the master’s program in computer science at the University of Ottawa may be eligible to fast-track directly into the doctoral program without writing a master’s thesis, provided the following conditions are met:

• Completion of 5 graduate courses (15 units) with a minimum average of A- (80%).
• Satisfactory progress in the research program.
• Written recommendation from the supervisor.
• Approval by the graduate studies committee and the vice-dean (graduate studies in the faculty).
• The student must request permission to fast-track during the fourth term of enrollment or earlier and must be enrolled in the PhD program in the fifth or, at the latest, in the sixth term. Following transfer, all of the requirements of the doctoral program must be met. Up to two of the Master's courses may be transferred to the doctoral program, if related to the Doctoral research. The total number of course units required is at least 18 (15 at master’s level plus 3 at PhD level).
• Students in the Accelerated Stream of the MCS are not eligible for fast-track to the PhD.

Requirements for this program have been modified. Please consult the  2023-2024 calendars  for the previous requirements.

The admissions committee and the student's advisory committee may impose additional requirements according to the student's background and research topic.

Students must meet the following requirements:

Students are required to enroll and present two seminars prior to submission of the thesis.

The comprehensive examination involves breadth and depth components.

The written thesis proposal must be defended at an oral examination.

The research thesis must be defended at an oral examination Students are responsible for ensuring they have met all of the thesis requirements .

Minimum Requirements

The passing grade in all courses is B.

Students who fail two courses (equivalent to 6 units), the thesis proposal, or the comprehensive exam or whose research progress is deemed unsatisfactory are required to withdraw.

Research Fields & Facilities

Located in the heart of Canada’s capital, a few steps away from Parliament Hill, the University of Ottawa is among Canada’s top 10 research universities.

uOttawa focuses research strengths and efforts in four Strategic Areas of Development in Research (SADRs):

• Canada and the World
• Molecular and Environmental Sciences

With cutting-edge research, our graduate students, researchers and educators strongly influence national and international priorities.

Research at the Faculty of Engineering

Areas of research:

• Chemical and Biological Engineering
• Civil Engineering
• Electrical Engineering and Computer Science
• Mechanical Engineering

For more information, refer to the list of faculty members and their research fields on Uniweb . 

IMPORTANT: Candidates and students looking for professors to supervise their thesis or research project can also consult the website of the faculty or department of their program of choice. Uniweb does not list all professors authorized to supervise research projects at the University of Ottawa.

CSI 5100 Data Integration (3 units)

Materialized and virtual approaches to integration of heterogeneous and independent data sources. Emphasis on data models, architectures, logic-based techniques for query processing, metadata and consistency management, the role of XML and ontologies in data integration; connections to schema mapping, data exchange, and P2P systems. This course is equivalent to COMP 5306 at Carleton University.

Course Component: Lecture

CSI 5101 Knowledge Representation (3 units)

KR is concerned with representing knowledge and using it in computers. Emphasis on logic-based languages for KR, and automated reasoning techniques and systems; important applications of this traditional area of AI to ontologies and semantic web. This course is equivalent to COMP 5307 at Carleton University.

CSI 5102 Topics in Medical Computing (3 units)

Introductory course on data structures, algorithms, techniques, and software development related to medical computing (in particular spatial modeling). Topics may include: computational geometry algorithms for cancer treatment, medical imaging, spatial data compression algorithms, dynamic programming for DNA analysis. This course is equivalent to COMP 5308 at Carleton University.

CSI 5105 Network Security and Cryptography (3 units)

Advanced methodologies selected from symmetric and public key cryptography, network security protocols and infrastructure, identification, anonymity, privacy technologies, secret-sharing, intrusion detection, firewalls, access control technologies, and defending network attacks. This course is equivalent to COMP 5406 at Carleton University.

Prerequisites: familiarity with basic concepts in networks, network security, and applied cryptography.

CSI 5106 Cryptography (3 units)

Security in encryption algorithms. Encryption and decryption. Entropy, equivocation, and unicity distance. Cryptanalysis and computational complexity. Substitution, transposition, and product ciphers. Symmetric ciphers: block and stream modes. Modular arithmetic. Public key cryptosystems. Factorization methods. Elliptic curve, lattice-based, and homomorphic cryptography. Proofs of security.

CSI 5107 Principle of Intelligent Transportation Systems (3 units)

Fundamental Concepts of ITS. Computer Information and Communication for ITS. The Backbone of ITS Communication, Network Topologies and Configurations. ITS Models and Evaluation Methods. Advanced Transportation Management Systems (ATMS). Advanced Traveler Information Systems (ATIS). Advanced Driver Assistant Systems. Data Stream Management System (DSMS) in the intelligent transportation Systems. Intelligent Traffic Control Algorithms. Traffic Demand Modeling and Analysis. Incident Detection and Collusion Avoidance Algorithms. Smart Mobility and GPS Localization Algorithms. Software Defined Network for ITS. Security & Privacy in ITS

CSI 5108 Introduction to Convex Optimization (3 units)

Mathematics of optimization: linear, nonlinear and convex problems. Convex and affine sets. Convex, quasiconvex and log-convex functions. Operations preserving convexity. Recognizing and formulating convex optimization problems. The Lagrange function, optimality conditions, duality, geometric and saddle-point interpretations. Least-norm, regularized and robust approximations. Statistical estimation, detector design. Adaptive antennas. Geometric problems (networks). Algorithms.

CSI 5110 Principles of Formal Software Development (3 units)

Methodologies in formal software specification, development, and verification. The use of theorem proving, automated deduction, and other related formal methods for software correctness. Applications in program verification and secure computation. This course is equivalent to COMP 5707 at Carleton University.

CSI 5111 Software Quality Engineering (3 units)

Software quality issues. Quality components and metrics. Software process quality. Software reliability engineering. Software design for testability. Requirements capture and validation. Systematic design validation; grey-box approach, test design, implementation and management, case studies in validation and verification of communications software. Object-oriented design and test. Theoretical aspects. This course is equivalent to COMP 5501 at Carleton University.

CSI 5112 Software Engineering (3 units)

Topics of current interest in Software Engineering, such as requirements engineering, precise and advanced modelling, development processes, change management, standards, and emerging types of applications. This course is equivalent to COMP 5207 at Carleton University.

CSI 5113 Foundations Programming Languages (3 units)

Advanced study of programming paradigms from a practical perspective. Paradigms may include functional, imperative, concurrent, distributed, generative, aspect- and object-oriented, and logic programming. Emphasis on underlying principles. Topics may include: types, modules, inheritance, semantics, continuations, abstraction and reflection. This course is equivalent to COMP 5001 at Carleton University.

CSI 5115 Database Analysis and Design (3 units)

The dimensional and multidimensional data models for data warehousing. Data dependencies and decomposition. Structure and use of data definition and manipulation languages. Database economics, engineering, deployment and evolution. Issues in integrity, security, the Internet and distributed databases. Relationships to decision support systems. This course is equivalent to COMP 5503 at Carleton University.

Course Component: Discussion Group, Laboratory, Lecture, Research, Seminar, Work Term, Theory and Laboratory, Tutorial

CSI 5116 Authentication and Software Security (3 units)

Specialized topics in security including advanced authentication techniques, user interface aspects, electronic and digital signatures, security infrastructures and protocols, software vulnerabilities affecting security, non-secure software and hosts, protecting software and digital content. This course is equivalent to COMP 5407 at Carleton University.

CSI 5118 Automated Verification and Validation of Software (3 units)

Topics in formal test derivation methods, test management, high-level, CASE-based verification and validation, data-flow & control-flow measures and metrics for assessing quality of designs and code, regression analysis & testing. This course is equivalent to COMP 5302 at Carleton University.

CSI 5121 Advanced Data Structures (3 units)

Simple methods of data structure design and analysis that lead to efficient data structures for several problems. Topics include randomized binary search trees, persistence, fractional cascading, self-adjusting data structures, van Emde Boas trees, tries, randomized heaps, and lowest common ancestor queries. This course is equivalent to COMP 5408 at Carleton University.

CSI 5122 Software Usability (3 units)

Design principles and metrics for usability. Qualitative and quantitative methods for the evaluation of software system usability: Heuristic evaluation, usability testing, usability inspections and walkthroughs, cognitive walkthroughs, formal usability experimentation. Ethical concerns when performing studies with test users. Economics of usability. Integration of usability engineering into the software engineering lifecycle. This course is equivalent to COMP 5301 at Carleton University.

CSI 5124 Computational Aspects of Geographic Information Systems (3 units)

Computational perspective of geographic information systems (GIS). Data representations and their operations on raster and vector devices: e.g., quadtrees, grid files, digital elevation models, triangular irregular network models. Analysis and design of efficient algorithms for solving GIS problems: visibility queries, point location, facility location. This course is equivalent to COMP 5204 at Carleton University.

CSI 5126 Algorithms in Bioinformatics (3 units)

Fundamental mathematical and algorithmic concepts underlying computational molecular biology; physical and genetic mapping, sequence analysis (including alignment and probabilistic models), genomic rearrangement, phylogenetic inference, computational proteomics and systemics modelling of the whole cell. This course is equivalent to COMP 5108 at Carleton University.

CSI 5127 Applied Computational Geometry (3 units)

Design and analysis of efficient algorithms for solving geometric problems in applied fields such as Geometric Network Design, Geometric Routing and Searching. Geometric spanners, Greedy spanners, Theta-Graphs, Yao-Graphs, Well-Separated Pair Decomposition, Delaunay Triangulations. Introduction to the game of Cops and Robbers. This course is equivalent to COMP 5409 at Carleton University.

CSI 5128 Swarm Intelligence (3 units)

Collective computation, collective action, and principles of self-organization in social agent systems. Algorithms for combinatorial optimization problems, division of labour, task allocation, task switching, and task sequencing with applications in security, routing, wireless and ad hoc networks and distributed manufacturing. This course is equivalent to COMP 5002 at Carleton University.

CSI 5129 Advanced Database Systems (3 units)

In-depth study on developments in database systems shaping the future of information systems, including complex object, object-oriented, object-relational, and semi-structured databases. Data structures, query languages, implementation and applications. This course is equivalent to COMP 5305 at Carleton University.

CSI 5131 Parallel Algorithms and Applications in Data Science (3 units)

Multiprocessor architectures from an application programmer's perspective: programming models, processor clusters, multi-core processors, GPUs, algorithmic paradigms, efficient parallel problem solving, scalability and portability. Projects on high performance computing in Data Science, including data analytics, bioinformatics, simulations. Programming experience on parallel processing equipment. This course is equivalent to COMP 5704 at Carleton University.

CSI 5134 Fault Tolerance (3 units)

Hardware and software techniques for fault tolerance. Topics include modeling and evaluation techniques, error detecting and correcting codes, module and system level fault detection mechanisms, design techniques for fault-tolerant and fail-safe systems, software fault tolerance through recovery blocks, N-version programming, algorithm-based fault tolerance, checkpointing and recovery techniques, and survey of practical fault-tolerant systems. This course is equivalent to COMP 5004 at Carleton University.

CSI 5135 Information Visualization and Visual Analytics (3 units)

Principles, techniques, technology and applications of information visualization for visual data analysis. Topics include human visual perception, cognitive processes, static and dynamic models of image semantics, interaction paradigms, big data visual analysis case studies. This course is equivalent to COMP 5209 at Carleton University.

CSI 5136 Computer Security and Usability (3 units)

Design and evaluation of security and privacy software with particular attention to human factors and how interaction design impacts security. Topics include current approaches to usable security, methodologies for empirical analysis, and design principles for usable security and privacy. This course is equivalent to COMP 5110 at Carleton University.

CSI 5137 Selected Topics in Software Engineering (Category E) (3 units)

Selected topics in Software Engineering (Category E), not covered by other graduate courses. Details will be available from the School at the time of registration. This course is equivalent to COMP 5900 at Carleton University.

CSI 5138 Selected Topics in Theory of Computing (Category T) (3 units)

Selected topics in Theory of Computing (Category T), not covered by other graduate courses. Details will be available from the School at the time of registration. This course is equivalent to COMP 5900 at Carleton University.

CSI 5139 Selected Topics in Computer Applications (Category A) (3 units)

Selected topics in Computer Applications (Category A), not covered by other graduate courses. Details will be available from the School at the time of registration. This course is equivalent to COMP 5900 at Carleton University.

CSI 5140 Selected Topics in Computer Systems (Category S) (3 units)

Selected topics in Computer Systems (Category S), not covered by other graduate courses. Details will be available from the School at the time of registration. This course is equivalent to COMP 5900 at Carleton University.

CSI 5142 Protocols for Mobile and Wireless Networks (3 units)

Link and network layer protocols of wireless networks; applications of wireless networks may be discussed. Topics may include: protocol implementation, mobile IP, resource discovery, wireless LANs/PANs, and Spreadspectrum. Courses CSI 6136 (SYSC 5306), CSI 5142 (COMP 5402) cannot be combined for units. This course is equivalent to COMP 5402 at Carleton University.

Precludes additional credit for SYSC 5306.

CSI 5146 Computer Graphics (3 units)

Principles and advanced techniques in rendering and modelling. Research field overview. Splines, subdivision surfaces and hierarchical surface representations. Physics of light transport, rendering equation and Bidirectional Reflectance Distribution Function. Classical ray tracing, radiosity, global illumination and modern hybrid methods. Plenoptic function and image-based rendering. This course is equivalent to COMP 5202 at Carleton University.

CSI 5147 Computer Animation (3 units)

Theories and techniques in 3D modeling and animation. Animation principles, categories, and history. Forward and inverse kinematics. Motion capture, editing and retargeting. Flexible bodies. Particle animation. Behavioral animation. Human modeling. Facial animation. Cloth animation and other sub-topics. This course is equivalent to COMP 5201 at Carleton University.

CSI 5148 Wireless Ad Hoc Networking (3 units)

Self-organized, mobile, and hybrid ad hoc networks. Physical, medium access, networks, transport and application layers, and cross-layering issues. Power management. Security in ad hoc networks. Topology control and maintenance. Data communication protocols, routing and broadcasting. Location service for efficient routing. This course is equivalent to COMP 5103 at Carleton University.

CSI 5149 Graphical Models and Applications (3 units)

Bayesian networks, factor graphs, Markov random fields, maximum a posteriori probability (MAP) and maximum likelihood (ML) principles, elimination algorithm, sum-product algorithm, decomposable and non-decomposable models, junction tree algorithm, completely observed models, iterative proportional fitting algorithm, expectation- maximization (EM) algorithm, iterative conditional modes algorithm, variational methods, applications. Courses CSI 5149 (COMP 5007), ELG 5131 (EAGJ 5131) and ELG 7177 (EACJ 5605) cannot be combined for units. This course is equivalent to COMP 5007 at Carleton University.

Permission of the Department is required.

CSI 5151 Virtual Environments (3 units)

Basic concepts. Virtual worlds. Hardware and software support. World modeling. Geometric modeling. Light modeling. Kinematic and dynamic models. Other physical modeling modalities. Multi-sensor data fusion. Anthropomorphic avatars. Animation: modeling languages, scripts, real-time computer architectures. Virtual environment interfaces. Case studies. Courses ELG 5124 (EACJ 5204), CSI 5151 (COMP 5205) cannot be combined for units. This course is equivalent to COMP 5205 at Carleton University.

CSI 5152 Evolving Information Networks (3 units)

Convergence of social and technological networks with WWW. Interplay between information content, entities creating it and technologies supporting it. Structure and analysis of such networks, models abstracting their properties, link analysis, search, mechanism design, power laws, cascading, clustering and connections with work in social sciences. This course is equivalent to COMP 5310 at Carleton University.

CSI 5153 Data Management for Business Intelligence (3 units)

Data management problems and information technology in decision making support in business environments. Topics include advanced data modeling, semantic modeling, multidimensional databases and data warehousing, on-line-analytical processing, elements of data mining, context in data management, data quality assessment, data cleaning, elements of business process modeling. This course emphasizes concepts and techniques rather than specific applications or systems/implementations. This course is equivalent to COMP 5111 at Carleton University.

CSI 5154 Algorithms for Data Science (3 units)

Algorithmic techniques to handle (massive/big) data arising from, for example, social media, mobile devices, sensors, financial transactions. Algorithmic techniques may include locality-sensitive hashing, dimensionality reduction, streaming, clustering, VC-dimension, external memory, core sets, link analysis and recommendation systems. This course is equivalent to COMP 5112 at Carleton University.

CSI 5155 Machine Learning (3 units)

Concepts, techniques, and algorithms in machine learning; representation, regularization and generalization; supervised learning; unsupervised learning; advanced methods such as support vector machines, online algorithms, neural networks, hidden Markov models, and Bayesian networks; curse of dimensionality and large-scale machine learning. Category T in course list. This course is equivalent to COMP 5116 at Carleton University.

Courses CSI 5155 , DTO 5100 , DTO 5101 , ELG 5255 , IAI 5100 , IAI 5101 , MIA 5100 , SYS 5185 cannot be combined for units.

CSI 5161 Principles of Distributed Simulation (3 units)

Distributed simulation principles and practices. Synchronization protocols: Optimistic vs Conservative, Deadlock detection in conservative simulations, Time warp simulation. Distributed interactive simulation: Data distribution management, Interest management, High Level Architectures (HLA), Run Time Infrastructure (RTI). Distributed web-based simulation. Distributed agent based simulation. Real time applications of distributed simulation. Distributed and collaborative virtual simulations. This course is equivalent to COMP 5606 at Carleton University.

CSI 5163 Algorithm Analysis and Design (3 units)

Topics of current interest in the design and analysis of computer algorithms for graph-theoretical applications; e.g. shortest paths, chromatic number, etc. Lower bounds, upper bounds, and average performance of algorithms. Complexity theory. This course is equivalent to COMP 5703 at Carleton University.

CSI 5164 Computational Geometry (3 units)

Study of design and analysis of algorithms to solve geometric problems; emphasis on applications such as robotics, graphics, and pattern recognition. Topics include: visibility problems, hidden line and surface removal, path planning amidst obstacles, convex hulls, polygon triangulation, point location. This course is equivalent to COMP 5008 at Carleton University.

CSI 5165 Combinatorial Algorithms (3 units)

Design of algorithms for solving problems that are combinatorial in nature, involving exhaustive generation, enumeration, search and optimization. Algorithms for generating basic combinatorial objects (permutations, combinations, subsets) and for solving hard optimization problems (knapsack, maximum clique, minimum set cover). Metaheuristic search, backtracking, branch-and-bound. Computing isomorphism of combinatorial objects (graphs), isomorph-free exhaustive generation. This course is equivalent to COMP 5709 at Carleton University.

CSI 5166 Applications of Combinatorial Optimization (3 units)

Topics in combinatorial optimization with emphasis on applications in Computer Science. Topics include network flows, various routing algorithms, polyhedral combinatorics, and the cutting plane method. This course is equivalent to COMP 5805 at Carleton University.

CSI 5167 Human-Computer Interaction Models, Theories and Frameworks (3 units)

A basis for graduate study in HCI with an emphasis on the application of theory to user interface design. Review of main theories of human behaviour relevant to HCI, including especially Cognitive Dimensions of Notations Framework, Mental Models, Distributed Cognition, and Activity Theory, and their application to design and development of interactive systems. This course is equivalent to COMP 5210 at Carleton University.

CSI 5168 Digital Watermarking (3 units)

Overview of recent advances in watermarking of image, video, audio, and other media. Spatial, spectral, and temporal watermarking algorithms. Perceptual models. Use of cryptography in steganography and watermarking. Robustness, security, imperceptibility, and capacity of watermarking. Content authentication, copy control, intellectual property, digital rights management, and other applications. This course is equivalent to COMP 5309 at Carleton University.

CSI 5169 Wireless Networks and Mobile Computing (3 units)

Computational aspects and applications of design and analysis of mobile and wireless networking. Topics include Physical, Link Layer, Media Access Control, Wireless, Mobile LANs (Local Area Networks), Ad-Hoc, Sensor Networks, Power Consumption optimization, Routing, Searching, Service Discovery, Clustering, Multicasting, Localization, Mobile IP/TCP (Internet Protocol/Transmission Control Protocol), File Systems, Mobility Models, Wireless Applications. Courses CSI 5169 , ELG 6168 cannot be combined for units. This course is equivalent to COMP 5304 at Carleton University.

CSI 5173 Data Networks (3 units)

Mathematical and practical aspects of design and analysis of communication networks. Topics include: basic concepts, layering, delay models, multi-access communication, queuing theory, routing, fault-tolerance, and advanced topics on high-speed networks, ATM, mobile wireless networks, and optical networks. This course is equivalent to COMP 5203 at Carleton University.

CSI 5174 Validation Methods for Distributed Systems (3 units)

Review of formal specification and description techniques for distributed and open systems. Verification techniques. Correctness proofs. Verification of general properties of distributed systems. Analysis and relief strategies. Testing techniques. Test generation strategies. Test architectures. This course is equivalent to COMP 5604 at Carleton University.

CSI 5175 Mobile Commerce Technologies (3 units)

Wireless networks support for m-commerce; m-commerce architectures and applications; mobile payment support systems; business models; mobile devices and their operating systems; mobile content presentation; security issues and solutions; relevant cross layer standards and protocols; case studies. Courses DTI 5175 , CSI 5175 cannot be combined for units. This course is equivalent to COMP 5220 at Carleton University.

CSI 5180 Topics in Artificial Intelligence (3 units)

Selected topics in Artificial Intelligence (A.I.); could include A.I. programming techniques, pattern matching systems, natural language systems, rule-based systems, constraint systems, machine learning systems, and cognitive systems. Applications could include areas in Finance, Medicine, Manufacturing, Smart Cities, Semantic Web, Healthcare, Fraud Detection, Intrusion Detection, Autonomous Vehicles, Opinion mining, Sentiment Analysis or similar areas. Assignments will be both (a) programming-oriented, requiring implementation and/or extensions of prototypes in Lisp and/or Prolog and (b) research-oriented, requiring readings of special topics in current A.I. journals. This course is equivalent to COMP 5100 at Carleton University.

CSI 5183 Evolutionary Computation and Artificial Life (3 units)

Study of algorithms based upon biological theories of evolution, applications to machine learning and optimization problems. Possible topics: Genetic Algorithms, Classifier Systems, and Genetic Programming. Recent work in the fields of Artificial Life (swarm intelligence, distributed agents, behavior-based AI) and of connectionism. This course is equivalent to COMP 5206 at Carleton University.

Precludes additional credit for COMP 4107.

CSI 5185 Statistical and Syntactic Pattern Recognition (3 units)

Topics include a mathematical review, Bayes decision theory, maximum likelihood and Bayesian learning for parametric pattern recognition, non-parametric methods including nearest neighbor and linear discriminants. Syntactic recognition of strings, substrings, subsequences and tree structures. Applications include speech, shape and character recognition. This course is equivalent to COMP 5107 at Carleton University.

CSI 5195 Ethics for Artificial Intelligence (3 units)

Students critically examine topics in applied AI ethics through the lens of contemporary philosophy and applied ethics texts, popular media articles, and technology case studies. Topics may include: bias and fairness; explainability; accountability; privacy; deception; trust/trustworthiness; and metaphors. Methods for applying ethical considerations in technology design are introduced through hands-on design projects. (Category E)

Courses CSI 5195 , DTI 5310 , DTO 5310 , SYS 5295 cannot be combined for units.

CSI 5200 Projects on Selected Topics (3 units)

CSI 5218 Uncertainty Evaluation in Engineering Measurements and Machine Learning (3 units)

Uncertainty, uncertainty propagation, Bayesian inference, sensor fusion, time series, Gaussian processes, integrating scientific/user knowledge into machine learning, neural networks for differential equations, probabilistic deep learning, sequential decision making. Case studies will be drawn from various fields including biomedical, autonomous vehicles, sensors, and signal processing.

The courses CSI 5218 , ELG 5218 cannot be combined for units.

CSI 5308 Principles of Distributed Computing (3 units)

Formal models of distributed environment; theoretical issues in the design of distributed algorithms; message and time complexity; problem solving in distributed settings. Problems discussed may include: coordination and control, information diffusion, leader election, consensus, distributed data operations, computing by mobile entities. This course is equivalent to COMP 5003 at Carleton University.

CSI 5311 Distributed Databases and Transaction Processing (3 units)

Principles involved in the design and implementation of distributed databases and distributed transaction processing systems. Topics include: distributed and multi-database system architectures and models, atomicity, synchronization and distributed concurrency control algorithms, data replication, recovery techniques, and reliability in distributed databases. This course is equivalent to COMP 5101 at Carleton University.

CSI 5312 Distributed Operating Systems (3 units)

Design issues of advanced multiprocessor distributed operating systems: multiprocessor system architectures; process and object models; synchronization and message passing primitives; memory architectures and management; distributed file systems; protection and security; distributed concurrency control; deadlock; recovery; remote tasking; dynamic reconfiguration; performance measurement, modeling, and system tuning. This course is equivalent to COMP 5102 at Carleton University.

CSI 5314 Object-Oriented Software Development (3 units)

Issues in modeling and verifying quality and variability in object-oriented systems. Testable models in model-driven and test-driven approaches. System family engineering. Functional conformance: scenario modeling and verification, design by contract. Conformance to non-functional requirements: goals, forces and tradeoffs, metrics. This course is equivalent to COMP 5104 at Carleton University.

CSI 5340 Introduction to Deep Learning and Reinforcement Learning (3 units)

Fundamental of machine learning; multi-layer perceptron, universal approximation theorem, back-propagation; convolutional networks, recurrent neural networks, variational auto-encoder, generative adversarial networks; components and techniques in deep learning; Markov Decision Process; Bellman equation, policy iteration, value iteration, Monte-Carlo learning, temporal difference methods, Q-learning, SARSA, applications. This course is equivalent to COMP 5340 at Carleton University.

CSI 5341 Learning-based Computer Vision (3 units)

Introduction to learning-based computer vision; statistical learning background; image processing and filtering primer; convolutional neural networks (CNNs), network layers, computer vision data sets and competitions; computer vision problems, in particular, image classification, detection and recognition, semantic segmentation, image generation, multi-view problems and tracking. This course is equivalent to COMP 5341 at Carleton University.

CSI 5342 Ubiquitous Sensing for Smart Cities (3 units)

Sensor and actuator networks. Dedicated and non-dedicated sensing. Vehicular sensing and smart transportation. Software Defined Things. Sensing as a service. Machine and deep learning-based misbehaviour detection. IoT-data analytics ecosystems. Federated Learning. AI-based security solutions. Auction and game theory concepts in ubiquitous sensing. This course is equivalent to COMP 5342 at Carleton University.

CSI 5343 AI-Enabled Communications (3 units)

Wireless networking fundamentals. Device to-device communications. Networking with cognitive radio. Cyber physical systems (CPS). Self-organization. Supervised and unsupervised learning. Reinforcement learning. Deep learning.This course is equivalent to COMP 5343 at Carleton University.

CSI 5344 Geometry Processing (3 units)

The course covers concepts, representations, and algorithms for analyzing and processing 3D geometric datasets. Topics include shape representations (e.g., triangle meshes, points clouds, and implicit functions), and the geometry processing pipeline covering the acquisition (e.g., with laser scanning or depth cameras), reconstruction, manipulation, editing, analysis, and fabrication (3D printing) of geometric models. This course is equivalent to COMP 5115 at Carleton University.

CSI 5345 Internet of Things (IoT) Security (3 units)

The course examines security challenges related to the Internet of Things (IoT), with a focus on consumer IoT devices, software aspects including engineering design, security of communications protocols and wireless access, cryptographic mechanisms, device integration and configuration, and security of IoT applications and platforms. This course is equivalent to COMP 5119 at Carleton University.

CSI 5346 Mining Software Repositories (3 units)

Introduction to the methods and techniques of mining software engineering data. Software repositories and their associated data. Data extraction and mining. Data analysis and interpretation (statistics, metrics, machine learning). Empirical case studies. This course is equivalent to COMP 5117 at Carleton University.

CSI 5347 Trends in Big Data Management (3 units)

Discussion of research papers on hot topics in the area of data management. The list of topics covered in the course generally spans: Data Exploration, Data Cleaning, Data Integration, Data Mining, Data Lake Management, Knowledge Graphs, Graph Processing, Question Answering, Blockchain, Crowdsourcing, Internet of Things, Text Processing, and Training via Weak Supervision. The common characteristic among all these topics is the large scale of data. This course is equivalent to COMP 5118 at Carleton University.

CSI 5350 Machine Learning for Healthcare (3 units)

Principles, techniques, technology and applications of machine learning for medical data such as medical imaging data, genomic data, physiological signals, speech and language. This course is equivalent to COMP 5113 at Carleton University.

CSI 5351 Quantum Communications and Networking (3 units)

Quantum communications and networking; the use of individual photons and teleportation to represent and transmit information. Theoretical (mathematical) principles. Practical aspects (implementation and software simulation) of quantum communications and networking. This course is equivalent to COMP 5114 at Carleton University.

CSI 5352 Internet Measurement and Security (3 units)

Measurement methodologies for understanding complex Internet phenomena and behaviors including: spread of vulnerabilities, remote network topologies, attack patterns, content popularity, Internet censorship, service quality, and adoption of security systems. Tools for efficient measurements, large-scale data analysis, stats, reproducibility of results. Ethical considerations. This course is equivalent to COMP 5500 at Carleton University.

CSI 5380 Systems and Architectures for Electronic Commerce (3 units)

E-commerce system architecture with a focus on relevant design patterns. Web servers, containers, and application frameworks. Web protocols, services, and client technologies. Scaleability through load balancing, clustering, and code optimization. Internationalization, accessibility, and privacy. Data mining and sharing approaches for digital targeted advertising. E-commerce user interface design and evaluation. Current research issues. Hands-on experience with an integrated set of current e-commerce tools. E-commerce development project. Courses EBC 5380, CSI 5380 cannot be combined for units. This course is equivalent to COMP 5405 at Carleton University.

CSI 5386 Natural Language Processing (3 units)

Overview of both rule-based or symbolic methods and statistical methods as approaches to Natural Language Processing (NLP), with more emphasis on the statistical ones. Applications such as information retrieval, text categorization, clustering, and statistical machine translation could be discussed. This course is equivalent to COMP 5505 at Carleton University.

CSI 5387 Data Mining and Concept Learning (3 units)

Concepts and techniques of data mining. Methods for data summarization and data preprocessing. Algorithms for finding frequent patterns and association analysis; classification; cluster analysis and anomaly detection. Model selection, model evaluation and statistical significance testing. Approaches for coping with Big Data. Selected applications of data mining and concept learning. This course is equivalent to COMP 5706 at Carleton University.

Permission of the Department is required. Courses CSI 5387 , DTO 5125, GNG 5125 cannot be combined for units.

CSI 5388 Topics in Machine Learning (3 units)

CSI 5389 Electronic Commerce Technologies (3 units)

Business models and technologies. Search engines. Cryptography. Web services and agents. Secure electronic transactions. Value added e-commerce technologies. Advanced research questions. Courses EBC5389, CSI5389 cannot be combined for units. This course is equivalent to COMP 5401 at Carleton University.

CSI 5390 Learning Systems from Random Environments (3 units)

Computerized adaptive learning for random environments and its applications. Topics include a mathematical review, learning automata which are deterministic/stochastic, with fixed/variable structures, of continuous/discretized design, with ergodic/absorbing properties and of estimator families. This course is equivalent to COMP 5005 at Carleton University.

CSI 5500 Projets en informatique (3 crédits)

Volet : Cours magistral

CSI 5501 Modèles formels de l'information (3 crédits)

CSI 5510 Principles de développement formel de logiciels (3 crédits)

Méthodologies pour la spécification, le développement et la vérification formels de logiciels. Utilisation d'assistants de preuves, de déduction automatisée et d'autres méthodes formelles visant l'exactitude de logiciel. Applications à la vérification de programmes et au calcul sécurisé. Ce cours est équivalent à COMP 5707 à la Carleton University.

CSI 5511 Génie de la qualité des logiciels (3 crédits)

Critères de la qualité des logiciels. Composantes et métriques de qualité. Qualité du processus de développement des logiciels. Génie de fiabilité des logiciels. Capture et validation d'exigences. Validation systématique de la conception; approche boîte-grise. Conception, implantation et gestion des tests. Étude de cas en validation et vérification des logiciels de communication. Conception orientée objet. Aspects théoriques. Ce cours est équivalent à COMP 5501 à la Carleton University.

CSI 5526 Algorithmes en bio-informatique (3 crédits)

Assemblage de l'ADN, recherche de gênes, comparaison de chaînes, alignement de séquences, structures grammaticales, structures secondaires et tertiaires. Les récents développements, tels que les puces d'ADN et de protéines. Travail additionnel requis dans le cas des étudiants inscrits sous la cote CSI 5526 .

Permission du Département est requise.

CSI 5537 Thème choisi en génie logiciel (catégorie E) (3 crédits)

Thèmes choisis en génie logiciel (catégorie E), non couverts par d'autres cours de deuxième cycle. Les détails seront disponibles à l'école au moment de l'inscription. Ce cours est équivalent à COMP 5900 à la Carleton University.

CSI 5538 Thème choisi en théorie de l'informatique (catégorie T) (3 crédits)

Thèmes choisis en théorie de l'informatique (catégorie T), non couverts par d'autres cours de deuxième cycle. Les détails seront disponibles à l'école au moment de l'inscription. Ce cours est équivalent à COMP 5900 à la Carleton University.

CSI 5539 Thème choisi en application informatique (catégorie A) (3 crédits)

Thèmes choisis en application informatique (catégorie A), non couverts par d'autres cours de deuxième cycle. Les détails seront disponibles à l'école au moment de l'inscription. Ce cours est équivalent à COMP 5900 à la Carleton University.

CSI 5540 Thème choisi en systèmes informatiques (catégorie S) (3 crédits)

Thèmes choisis en systèmes informatiques (catégorie S), non couverts par d'autres cours de deuxième cycle. Les détails seront disponibles à l'école au moment de l'inscription. Ce cours est équivalent à COMP 5900 à la Carleton University.

CSI 5555 Apprentissage machine (3 crédits)

Concepts, techniques et algorithmes en apprentissage machine; représentation, régularisation et généralisation; apprentissage supervisé; apprentissage non supervisé; méthodes avancées telles que les machines à vecteur de support, les algorithmes en ligne, les réseaux de neurones; les modèles de Markov cachés et les réseaux bayésiens; le fléau de la dimensionnalité et l'apprentissage machine à grande échelle. Catégorie T dans la liste de cours.

CSI 5561 Sujets en simulation et en optimisation des systèmes (3 crédits)

CSI 5563 Analyse et conception des algorithmes (3 crédits)

CSI 5565 Algorithmes combinatoires (3 crédits)

Conception d'algorithmes pour résoudre des problèmes de nature combinatoire (génération exhaustive, énumération, recherche et optimisation). Algorithmes pour générer des objets combinatoires de base (permutations, combinaisons, sous-ensembles) et pour résoudre des problèmes d'optimisation difficiles (knapsack, clique maximum, couverture minimum). Recherche métaheuristique, retour arrière, branch-and-bound. Calcul de l'isomorphisme des objets combinatoires (graphes), génération exhaustive sans isomorphes. Ce cours est équivalent à COMP 5709 à l'Université Carleton.

CSI 5571 Télématique : Concepts et logiciels (3 crédits)

CSI 5580 Sujets en intelligence artificielle (3 crédits)

Thèmes choisis en intelligence artificielle (I.A.); pourrait inclure des techniques de programmation en intelligence artificielle, des systèmes d'appariement de formes, des systèmes à langage naturel, des systèmes à base de règles, des systèmes de contraintes, des systèmes d'apprentissage automatique et des systèmes cognitifs. Les applications peuvent couvrir les domaines de la finance, de la médecine, de la fabrication, des villes intelligentes, du Web sémantique, de la détection de fraudes ou d’intrusion, des véhicules autonomes, de l'analyse d’opinion, de l'analyse de sentiments ou d’autres domaines similaires. Les devoirs seront à la fois (a) axés sur la programmation, exigeant l'implémentation et/ou l'extension de prototypes (b) axés sur la recherche, nécessitant des lectures de sujets spéciaux dans des revus d'I.A. contemporaines. Ce cours est équivalent à COMP 5100 à l'Université Carleton.

CSI 5780 Systèmes et architectures des logiciels pour le commerce électronique (3 crédits)

Architecture du système de commerce électronique et patrons de conception. Serveurs Web, conteneurs et cadres d'application. Protocoles, services, et technologies de client Web. Évolutivité grâce à l'équilibrage de la charge, au clustering et à l'optimisation du code. Internationalisation, accessibilité et confidentialité. Méthodes d'exploration et de partage de données pour la publicité ciblée numérique. Conception et évaluation de l'interface utilisateur pour le commerce électronique. Problèmes de recherche actuels. Expérience pratique avec un ensemble intégré d'outils de commerce électronique actuels. Projet de développement du commerce électronique. Les cours EBC 5380, CSI 5380 ne peuvent pas être combinés pour les unités. Ce cours est équivalent à COMP 5405 à la Carleton University.

Prerequisite: CSI 5389

CSI 5787 Fouille des données et apprentissage des concepts (3 crédits)

Aspects conceptuels et techniques de l’exploration des données. Méthodes pour l'agrégation et le prétraitement des données. Algorithmes d'extraction de patrons et analyse des règles d'association; partitionnement des données et détection des anomalies. Sélection et évaluation des modèles et tests de signification statistique. Approches pour composer avec les mégadonnées. Choix d'applications en exploration des données et en extraction des concepts.

CSI 5789 Technologies du commerce électronique (3 crédits)

Introduction aux modèles et technologies d'entreprise. Moteurs de recherche. Cryptographie. Services Web et agents. Transactions électroniques sécurisées. Technologies du commerce électronique à valeur ajoutée. Questions de recherche avancées. Ce cours est équivalent à COMP 5401 à la Carleton University.

Prerequisite: CSI 4110 or equivalent.

CSI 5900 Projets de recherche en informatique / Graduate Projects in Computer Science (3 crédits / 3 units)

Ce cours est équivalent à COMP 5902 à la Carleton University. / This course is equivalent to COMP 5902 at Carleton University.

Volet / Course Component: Recherche / Research

CSI 5901 Études dirigées / Directed Studies (3 crédits / 3 units)

A course of independent study under the supervision of a member of the School of Computer Science. Ce cours est équivalent à COMP 5901 à la Carleton University. / This course is equivalent to COMP 5901 at Carleton University.

CSI 5903 Stage en commerce électronique / Electronic Commerce Work Term (3 crédits / 3 units)

Expérience en milieu de travail. Noté S (satisfaisant) ou NS (non satisfaisant) selon les résultats du rapport écrit et l'évaluation de l'employeur. Préalable : être accepté au programme de certificat en commerce électronique (option technologie) et recevoir la permission du Comité du programme. / Practical experience. Graded S (Satisfactory) / NS (Not satisfactory), to be based on the grades obtained for the written report as well as on the evaluations of the employer.

Volet / Course Component: Cours magistral / Lecture

Permission du Département est requise. / Permission of the Department is required.

CSI 5904 Projet de recherche avancé en commerce électronique / Graduate Project in Electronic Commerce (3 crédits / 3 units)

Projet sur un sujet précis en commerce électronique mené sous la direction d'un professeur. Les cours CSI 5904 , CSI 5903 ne peuvent être combinés pour l'obtention de crédits. / Project on a specific topic in electronic commerce under the supervision of a professor. Courses CSI 5904 , CSI 5903 cannot be combined for units.

Exclusion: CSI 5903 .

CSI 6900 Projets de recherche intensive en informatique / Intensive Graduate Projects in Computer Science (6 crédits / 6 units)

Cours de six crédits s'échelonnant sur une période de deux sessions. L'envergure du projet de recherche exigé dans ce cours est deux fois plus grande que dans le cas de CSI 5900 . Les cours CSI 6900 , CSI 5900 ne peuvent être combinés pour l'obtention de crédits. Cours ouvert uniquement aux étudiants inscrits à la maîtrise sans thèse. Ce cours est équivalent à COMP 5903 à la Carleton University. / A two-session course. The project is twice the scope of projects in CSI 5900 . Courses CSI 6900 , CSI 5900 cannot be combined for units. Not to be taken in the thesis option. This course is equivalent to COMP 5903 at Carleton University.

CSI 7131 Advanced Parallel and Systolic Algorithms (3 units)

Continuation of CSI 5131 (COMP 5704). This course is equivalent to COMP 6100 at Carleton University.

CSI 7160 Advanced Topics in the Theory of Computing (3 units)

This course is equivalent to COMP 6601 at Carleton University.

CSI 7161 Advanced Topics in Programming Systems and Languages (3 units)

This course is equivalent to COMP 6603 at Carleton University.

CSI 7162 Advanced Topics in Computer Applications (3 units)

This course is equivalent to COMP 6604 at Carleton University.

CSI 7163 Advanced Topics in Computer Systems (3 units)

This course is equivalent to COMP 6605 at Carleton University.

CSI 7170 Advanced Topics in Distributed Computing (3 units)

This course is equivalent to COMP 6602 at Carleton University.

CSI 7314 Advanced Topics in Object-Oriented Systems (3 units)

Advanced object-oriented software engineering, in particular the issues of reuse and testing. Sample topics include: interaction modeling; class and cluster testing; traceability; design patterns and testing; the C++ standard template library. Students will carry out research. This course is equivalent to COMP 6104 at Carleton University.

CSI 7561 Études avancées en systèmes et langages de programmation (3 crédits)

Ce cours est équivalent à COMP 6603 à la Carleton University.

CSI 7900 Projets de recherche en informatique / Graduate Projects in Computer Science (3 crédits / 3 units)

Ce cours est équivalent à COMP 6902 à la Carleton University. / This course is equivalent to COMP 6902 at Carleton University.

CSI 7901 Études dirigées / Directed Studies (3 crédits / 3 units)

Ce cours est équivalent à COMP 6901 à la Carleton University. / This course is equivalent to COMP 6901 at Carleton University.

CSI 9901 Colloque / Seminar

Volet / Course Component: Séminaire / Seminar

CSI 9902 Colloque / Seminar

CSI 9997 Proposition de thèse de doctorat / Doctoral Thesis Proposal

Within 8 terms following initial registration in the program, a document, generally defining the problem addressed, relating it to the literature, outlining the hypotheses, goals, research methodology, initial results and validation approach, must be submitted to an examination committee and successfully defended. Ce cours est équivalent à COMP 6908 à la Carleton University. This course is equivalent to COMP 6908 at Carleton University.

CSI 9998 Examen général de doctorat / Ph.D. Comprehensive

A committee must be assembled and must approve at least 3 topics for written examination: typically, a major and two minor areas. An oral examination occurs if the written exam is passed. Both elements must take place within the first 4 terms following initial registration in the program. The comprehensive examination may be failed, passed conditionally (i.e., with extra course requirements) or passed unconditionally. If failed, this course may be retaken at most one time. Ce cours est équivalent à COMP 6907 à la Carleton University. This course is equivalent to COMP 6907 at Carleton University.

Undergraduate Studies

For more information about undergraduate studies at the University of Ottawa, please refer to your faculty .

Graduate and Postdoctoral Studies

For more information about graduate studies at the University of Ottawa, please refer to your academic unit .

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We have 5 Computer Science PhD Projects, Programmes & Scholarships in Canada

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Computer Science PhD Projects, Programmes & Scholarships in Canada

Dalhousie University

Halifax is home to leading tech research, modelling the physics of plant cell growth - start fall 2024, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Funded PhD Project (Students Worldwide)

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Kinetic ART Enhancement PhD Project: Monte-Carlo Code

Funded phd / msc in advanced electron microscopy, funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

Canada PhD Programme

A Canadian PhD usually takes 3-6 years. Programmes sometimes include taught classes and training modules followed by a comprehensive examination. You will then carry on to research your thesis, before presenting and defending your work. Programmes are usually offered in English, but universities in Québec and New Brunswick may teach in French.

The design of alternative models of electronic structure in the ground and excited states

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Computer Science (MSc, PhD)

Part of the Faculty of Science

Program Overview

Format : Full-time (MSc, PhD)

Degree Earned : Master of Science or PhD

Computer science is an exciting, rapidly evolving discipline that impacts our everyday lives in innumerable ways. Graduate degree-holders in computer science are in high demand. Graduates from our programs have a wide range of exciting career options in industry and academia.

Careers include but are not limited to:

• software developer
• data scientist
• database analyst
• computer vision scientist
• information technologist

Our faculty actively collaborate with industrial partners, which makes Toronto Metropolitan University’s central downtown location advantageous. It provides walking distance access to Toronto’s vibrant and rapidly growing start-up community, major companies, financial institutions and research hospitals.

The program provides funding to each domestic thesis student. Typical funding packages are outlined below.

At a Glance

Admissions information.

• Completion of a four-year undergraduate in computer science (or equivalent degree) from an accredited institution
• Minimum grade point average (GPA) or equivalent of 3.00/4.33 (B) in the last two years of study
• Statement of intent
• Transcripts
• Two letters of recommendation
• English language proficiency requirement
• Completion of a master’s degree in computer science or a closely related discipline from an accredited institution
• Minimum grade point average (GPA) or equivalent of 3.33/4.33 (B+)
• Three letters of recommendation

More information on  admission requirements . Due to the competitive nature of our programs, it is not possible to offer admission to everyone who applies that meets the minimum entrance requirements for the program. 

Program-specific requirements

Check Application Deadline

Students are encouraged to submit applications prior to the first consideration date to increase their chances of securing financial support for their graduate studies. Applications received after the first consideration date will be accepted and reviewed based on spaces remaining in the program.

See application dates .

Financing Your Studies

For detailed graduate tuition and fees information please visit  Fees by Program .

For information on scholarships, awards and financing your graduate studies visit  Financing Your Studies.

Research Areas

The Computer Science Graduate faculty conduct research in a wide range of subjects, including:

• Artificial Intelligence
• Augmented and Virtual Reality
• Computer Graphics
• Computer Vision
• Cyber-security
• Data Mining
• Data Science
• Machine Learning
• Software Engineering

Computer Science (MSc, PhD) graduate program calendar

Graduate Admissions

Admissions information and how to apply

Graduate Studies Admissions Office 11th Floor, 1 Dundas Street West Toronto, ON Telephone: 416-979-5150 Email:  [email protected]

For information specific to programs, please see the program contact information below.

Program Contacts

Dr. Alex Ferworn Graduate Program Director PhD Research areas: Computational public safety: Urban Search and Rescue (USAR) and Chemical, Biological, Radiological and Nuclear explosives (CBRNe) applications, serious gaming, mobile/autonomous/teleoperated robotics, cyber operations, network applications, entrepreneurship and innovation, physical computation, digital media, and algorithms. Telephone: 416-979-5000 , ext. 556968 Email: [email protected]

Norm Pinder Graduate Program Administrator Telephone: 416-979-5000 , ext. 552656 Email:  [email protected]

Student Profile:  VICE Canada feature  (external link) 

Jimmy Tran (computer science PhD student) designed and built a robot used by archaeologists to explore dangerous tombs in el-Hibeh, Egypt.

Find curriculum, course descriptions and important dates for Computer Science (MSc, PhD).

Once you’ve made an informed choice about which program(s) you are going to apply to, preparing your application requires careful research and planning.

At Toronto Metropolitan University, we understand that pursuing graduate studies is a significant financial investment. Funding comes from a combination of employment contracts (as a teaching assistant), scholarships, awards and stipends. There are a number of additional funding sources – internal and external – available to graduate students that can increase these funding levels.

As an urban innovation university, Toronto Metropolitan University offers 60+ cutting-edge, career-oriented graduate programs, as well as 125+ research centres, institutes and labs, in a wide range of disciplines. Our close connections with industry, government and community partners provide opportunities to apply your knowledge to real-world challenges and make a difference.

PhD Computational Sciences

The objective of the PhD in Computational Sciences is to produce interdisciplinary scholars who are capable of tackling emerging problems in the sciences and humanities through investigation, advanced research and application of current computer technologies. Students in the program will have the opportunity to study computer science within the context of another discipline commensurate with their own interests and career goals. These disciplines include but are not limited to the following: Economics, Engineering, English, Geography, History, Integrative Biology, Mathematics and Statistics, Pathobiology, Psychology, and Veterinary Medicine.

The PhD in Computational Sciences is a full­-time, four-­year program (12 semesters of continuous enrollment) during which students will complete the Technical and Communication Research Methodology course (CIS*6890) and any additional graded courses or modules assigned by their Advisory Committee. Students will also give two public seminars, pass a qualifying exam , conduct research and successfully defend a written PhD thesis at the final oral examination. Our PhD will prepare students for both academia and industry. Applicants also have the option to apply for a collaborative specialization in One Health while pursuing a thesis-based PhD degree in Computational Sciences. Each PhD candidate will conduct thesis research by working closely with TWO chosen faculty research advisors , a School of Computer Science (SoCS) advisor and an Application Discipline (AD) co-advisor (from discipline outside of computer science), who will share equal responsibility in supervising the candidate's research.

All applicants must identify a secured SoCS advisor AND AD co-advisor BEFORE applying to the program, and these confirmed advisors must be included in the application's Statement of Interest. Finding a SoCS and AD research advisor is mandetory for admission to the PhD.CSCI program and is the responsibility of the applicant.

You can review SoCS faculty Areas of Research and current available opportunities to assist you in finding a suitable SoCS faculty research advisor.

Tips for Finding a Research Advisor Note for International Applicants: In light of the recent announcement from the Canadian Government ( IRCC ) limiting the number of issues international student visas, we want to ensure our international graduate applicants that as of current, students applying to our graduate programs (MSc, MCTI, PhD) remain unaffected by this new policy. For more information about Canadian study permit policies, please connect with a member of our International Student Advising team.

How to Apply

Please apply online at Applying to Guelph.

Application Deadlines

Available spaces in the PhD program fill quickly, so it is in your best interest to submit your application and all required supporting documentation early. Our application deadline dates for each semester of enrollment are as follows:*

• Winter 2025: October 1, 2024
• Spring 2025: February 1, 2025
• Fall 2025: June 1, 2025

*The application deadlines are for both Domestic and International applicants. We strongly encourage International applicants to apply a minimum of 9 months in advance of the semester's start date to ensure study permits can be processed in time for admission. Please see the Office of Graduate & Postdoctoral Studies (OGPS) Recommendations for International Applicants for suggested application timeline.

Please note: Application processing times may vary and take approximately two to three months for decision. Please also ensure you have all required application documentation submitted by the specified deadline, or your application will be considered incomplete.

Applicants must meet the minimum admission requirements of both the University and the School of Computer Science (SoCS). For admission to the PhD in Computational Sciences program, entrants require the following:

• Thesis-based master's degree: Admission to the PhD program normally requires a recognized thesis-based master’s degree. Applicants possessing a recognized course-based master's degree may be considered if the applicant demonstrates an outstanding academic record and research accomplishments through publications in scholarly journals and/or conferences. We do not require students entering the program to have a credential in Computer Science. Such students are required to identify their experience using computerized techniques and demonstrate that they have the necessary background to complete the tasks outlined in the Statement of Interest.  
• Minimum B average: Applicants are required to have a minimum average of 75% ('B') during the previous two years of full-time university study for an accredited university graduate degree . For information on international degree admission requirements, please see:  International Credential Evaluation and select country. Admission average is calculated using the last 2 years of university-level study.  
• TWO faculty research advisors: Each PhD candidate will conduct thesis research by working closely with two chosen faculty research advisors , a School of Computer Science (SoCS) advisor and an Application Discipline (AD) co-advisor, who will share equal responsibility in supervising the candidate's research. At least one of the advisors must be regular graduate faculty member at the University of Guelph. A chosen SoCS advisor and AD co-advisor must be established BEFORE applying to the PhD.CSCI program, and must be included in the application's Statement of Interest. You can review SoCS faculty Areas of Research to assist you in finding a suitable SoCS faculty research advisor. Please also review Tips for Finding a Research Advisor to assist you in communicating effectively with our SoCS faculty.  
• English proficiency test: An English proficiency test will be required for any applicant whose first language is not English.  

In addition to the Faculty of Graduate Studies Requirements, the School of Computer Science (SOCS) requires all of the documentation noted on the application page including:

• Academic Transcripts
• A current resume or CV (including publications)
• The applicant must identify their potential supervisors (SoCS advisor and AD co-advisor) and explain their choice
• They should describe the general area of research in which they are interested
• The area must be at the crossroads between computer science and another discipline (the “application discipline”) in the sciences, social sciences, humanities, etc.
• They may also describe a specific research problem in the area and their initial ideas on how to approach it
• They should clearly explain the importance and interdisciplinary nature of the area or problem
• They should summarize the related research and refer to publications where appropriate
• An applicant without a master’s or bachelor’s degree in computer science should highlight their computational knowledge and experience (e.g., computer science courses taken; use of Matlab, Mathematica, R, Maple, Weka)
• An applicant without a master’s or bachelor’s degree in the application discipline should highlight their knowledge and experience in that discipline
• The applicant should list any other reasons why they consider themselves a strong applicant
• Three Academic References
• A test of English proficiency is required of all applicants whose first language is not English. The English language requirements for the PhD program are higher than other programs.  Required scores are shown below:
• TOEFL (and TOEFL Special Home Edition ): 100, speaking and writing 25, at least 21 in each category
• IELTS (and Computer-delivered IELTS ): 7.0, at least 6.5 for each component
• Pearson Test of English Academic (PTE-A): 60, a minimum score of 60 must be achieved in each individual component.
• Canadian Academic English Language Test (CAEL): 70, writing and speaking 70, no score lower than 60
• University of Guelph English Language Certificate at the Advanced Level
• DuoLingo Test: 120

*Please note, the test may not be more than two years old. The proof of English proficiency requirement may be waived in exceptional circumstances. For example, applicants may be eligible for an English Waiver who have conferred a degree in a country where English is the native language AND in a university where English is the language of instruction (e.g. Canada, UK, USA, Australia, etc.). School of Computer Science Graduate Admissions Committee approval is required.

6. The Declaration of Committement form is an additional document required for applicants of the One Health Collaborative Specialization ONLY

7. The GRE is NOT required for admission

Fees & Funding

Tuition fees.

Visit Guelph Graduate Fees for the approximate costs of studying at the University of Guelph. Please also review the cost of living information for domestic and international students to determine the approximate cost of living fees (as these are in addition to tuition).

Guaranteed Funding

Normally, PhD students are (at minimum) partially funded by the School of Computer Science. That is, a minimum funding stipend of $25,000 per year (maximum 4 years) is guaranteed to all international and domestic PhD students who are able to secure a graduate faculty advisor and admission to the program. Funding is provided as a combination of Graduate Teaching Assistantships (GTAs) and Graduate Research Assistantships (GRA).

Scholarships

Applicants are strongly encouraged to apply for the following scholarships:

• Natural Sciences and Engineering Research Council (NSERC): Domestic students only; deadlines are normally in the fall
• Ontario Graduate Scholarship (OGS): Domestic and International students; deadlines are normally in early winter

For more information about types of funding and scholarships available, please visit fees, funding and scholarships.

For more information and requirements, please visit the PhD in Computational Sciences Graduate Calendar , or download our Program Brochure.

Please note, we will not be able to pre-evaluate any documents, and must receive all the required documentation before your application can be evaluated. If you need further assistance, please contact our Graduate Program Assistant .

86 Best universities for Computer Science in Canada

Updated: February 29, 2024

• Art & Design
• Computer Science
• Engineering
• Environmental Science
• Liberal Arts & Social Sciences
• Mathematics

Below is a list of best universities in Canada ranked based on their research performance in Computer Science. A graph of 37.9M citations received by 1.31M academic papers made by 86 universities in Canada was used to calculate publications' ratings, which then were adjusted for release dates and added to final scores.

We don't distinguish between undergraduate and graduate programs nor do we adjust for current majors offered. You can find information about granted degrees on a university page but always double-check with the university website.

1. University of Toronto

For Computer Science

2. University of British Columbia

3. McGill University

4. University of Alberta

5. University of Waterloo

6. University of Montreal

7. University of Calgary

8. McMaster University

9. Western University

10. University of Ottawa

11. Queen's University

12. Simon Fraser University

13. Laval University

14. University of Manitoba

15. Concordia University

16. Dalhousie University

17. Carleton University

18. York University

19. University of Victoria

20. University of Saskatchewan

21. University of Guelph

22. Polytechnic School of Montreal

23. University of Quebec in Montreal

24. University of New Brunswick

25. Memorial University of Newfoundland

26. Ryerson University

27. University of Sherbrooke

28. University of Windsor

29. University of Regina

30. School of Higher Technology - University of Quebec

31. Wilfrid Laurier University

32. HEC Montreal

33. University of Lethbridge

34. Brock University

35. Ontario Tech University

36. Lakehead University

37. Royal Military College of Canada

38. University of Quebec, Trois-Rivieres

39. Trent University

40. Laurentian University

41. Saint Mary's University

42. St. Francis Xavier University

43. University of Winnipeg

44. Acadia University

45. University of Moncton

46. University of Quebec in Outaouais

47. University of Quebec at Chicoutimi

48. University of Northern British Columbia

49. University of Prince Edward Island

50. Brandon University

51. University of Quebec

52. University of Quebec in Rimouski

53. Thompson Rivers University

54. Mount Royal University

55. Mount Allison University

56. Mount Saint Vincent University

57. Bishop's University

58. Cape Breton University

59. MacEwan University

60. Nipissing University

61. British Columbia Institute of Technology

62. University of Quebec, Abitibi-Temiscamingue

63. Royal Roads University

64. Quest University Canada

65. Concordia University of Edmonton

66. University of the Fraser Valley

67. Vancouver Island University

68. Trinity Western University

69. Kings University in Canada

70. Algoma University

71. OCAD University

72. Kwantlen Polytechnic University

73. National School of Public Administration

74. St. Thomas University - Canada

75. University Canada West

76. University of Saint-Boniface

77. Redeemer University College

78. SAIT Polytechnic

79. Northern Alberta Institute of Technology

80. Capilano University

81. University of King's College

82. Emily Carr University of Art and Design

83. First Nations University of Canada

84. Ambrose University

85. University of Sainte-Anne

86. Crandall University

The best cities to study Computer Science in Canada based on the number of universities and their ranks are Toronto , Vancouver , Montreal , and Edmonton .

Computer Science subfields in Canada

Bachelor's Degree in Computer Science

Why pursue a bachelor's degree in computer science.

The concentration in Computer Science is designed to teach students skills and ideas they will use immediately and in the future. Because information technology affects every aspect of society, graduates with computer science degrees have open to them an enormous variety of careers—engineering, teaching, medicine, law, basic science, entertainment, management, and countless others. 

At Harvard College, students choose a "concentration," which is what we call a major. All prospective undergraduate students, including those intending to study engineering and applied sciences, apply directly to Harvard College . During your sophomore spring you’ll declare a concentration, or field of study. You may choose from 50 concentrations and 49 secondary field (from Harvard DSO website ).

All undergraduates in Computer Science at Harvard are candidates for the Bachelor of Arts degree (A.B.) . With the knowledge that it requires extra course work, you can consider the more intensive  A.B./S.M. option  through a concurrent masters degree.

Learn about our Computer Science concentrators  >

Apply to Harvard College  >

A.B. in Computer Science

The basic degree requirements are eleven to fourteen 4-credit courses in mathematics, theoretical computer science, computer software, and other areas of computer science. Math courses cover linear algebra, single variable calculus and probability/statistics. Students who place out of part or all of the introductory calculus sequence, Mathematics 1ab, reduce their concentration requirements to 11 courses.

Computer Science Secondary Field

A lightweight way of getting official recognition within Harvard for work in two fields is to do one or the other as a secondary field. For Computer Science, this involves taking 4 courses in the secondary field. Learn more about the  computer science secondary field .

A.B./S.M. in Computer Science

Our  AB/SM degree program  is for currently enrolled Harvard College students only. Students who are eligible for  Advanced Standing  on the basis of A.P. tests before entering Harvard may be able to apply for admission to the S.M. program of the Graduate School of Arts and Sciences and graduate in four years with both a bachelor’s and master’s degree (not necessarily in the same field).

Beginning with the class of 2022, students have the opportunity to apply to the Graduate School of Arts and Sciences for a master’s degree pursued concurrently with the bachelor’s degree. As part of the  concurrent degree program , students will be allowed to double-count up to sixteen credits (normally, four courses) for the Bachelor of Arts and the Master of Science. An undergraduate pursuing the concurrent degree must complete both of these degrees by the end of eight terms of residency, or the equivalent.

The Mind, Brain, and Behavior Program (MBB)

Students interested in addressing questions of neuroscience and cognition from the perspective of computer science may pursue a special program of study affiliated with the University-wide Mind, Brain, and Behavior Initiative, that allows them to participate in a variety of related activities. (Similar programs are available through the Anthropology, History and Science, Human Evolutionary Biology, Linguistics, Neurobiology, Philosophy, and Psychology concentrations.) Requirements for this honors-only program are based on those of the computer science Requirements for Honors Eligibility. See the  handbook entry  for more information and also  Frequently Asked Questions about the MBB Track . This is an honors track program: students are eligible for English Honors.

Why study CS at Harvard? What’s different about pursuing CS in a liberal arts setting?

Get the answer to these questions and learn more about CS .

Prerequisites

Learn about the prerequisites for the concentration on our  First-Year Exploration page . Students interested in concentrating in computer science can refer to our Sophomore Advising page  and request to be matched with a Peer Concentration Advisor  (PCA). PCAs serve as peer advisors for pre-concentrators (and current concentrators), providing a valuable perspective and helping students to discover additional resources and opportunities.

Requirements

Learn more about the Computer Science requirements >

View current Computer Science courses . >

View sample plans of study. >

Tags for Computer Science courses. > 

Research Opportunities in Computer Science

As part of your Bio/Biomedical Engineering coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to take part in or participate in some extraordinary projects.  Learn more about research opportunities at Harvard SEA S.

Learn about the research interests of our Computer Science faculty .

Computer Science Career Paths

Learn about potential career paths for students for students concentrating in Computer Science . 

Computer Science & Society

Harvard Computer Science has several programs that allow undergraduate students to think about the broader issues in tech and CS.

Computer Science Clubs and Organizations

SEAS-affiliated student organizations are critical to the overall growth of our concentrators as engineering and applied science professionals. These organizations enable our students to pursue passion projects and events in areas of interest that are complementary to the current formal academic curriculum. Learn more about computer science student clubs and organizations .

In Computer Science

• First-Year Exploration
• Concentration Information
• Secondary Field
• Senior Thesis
• AB/SM Information
• Student Organizations
• How to Apply
• PhD Timeline
• PhD Course Requirements
• Qualifying Exam
• Committee Meetings (Review Days)
• Committee on Higher Degrees
• Research Interest Comparison
• Collaborations
• Cross-Harvard Engagement
• Lecture Series
• Clubs & Organizations
• Centers & Initiatives
• Alumni Stories