MASc. Program

The Master of Applied Science (MASc) program offers opportunities for advanced research in the areas of Mechanical Engineering, Biological Engineering, Computer Engineering, Environmental Engineering, Engineering Systems and Computing, and Water Resources Engineering. The MASc program is research thesis based and is available in full-time as well as part-time studies. The program provides advanced training in the engineering sciences and research methodology through a combination of course work, applied research, and thesis writing.

Program Fees

Please refer to Student Financial Services for further information about Graduate Fees.

Degree Requirements

The prescribed program of study must consist of no fewer than 2.0 credits, of which at least 1.0 must be engineering graduate courses. Of the remaining 1.0 credits, 0.5 credits must be at the graduate level, and the other 0.5 credits may be graduate credits or senior undergraduate engineering credits. Depending on the student's background, the advisory committee may specify more than four courses, including undergraduate make-up courses. If make-up courses are deemed necessary, they will be considered additional courses.

MASc Final Examination

Each candidate must submit a thesis based upon research of an approved topic. The thesis must demonstrate the candidates capacity for original and independent work and should include a critical evaluation of previous research in the field. The thesis should emphasize new conclusions drawn from the candidates research. The SOE Associate Director, Graduate Studies, will arrange for an Examination Committee as outlined in the Graduate Calendar. The duration of the examination will not exceed 2.5 hours and will follow the format outlined in the School of Engineering Guidelines for Oral Examination of Thesis.

Typical Program Timeline (Full-Time)

Typical Program Timeline for MASc Program    
Semester 1 - 3 Coursework

4 courses (minimum 2.0 credits)

  • 1.5 credits must be graduate level
  • 1.0 credits must be from engineering graduate courses
Semester 4 - 6 Research

Research for thesis

  • Successful completion and defense of a thesis based upon research of an approved topic


Collaborative International Development Studies (IDS) Designation

Students in the Environmental Engineering and Water Resources Engineering fields can choose to combine their MASc with an IDS designation (MASc.ENGG + IDEV). The collaborative IDS specialization offers an interdisciplinary framework for the study of international development that combines training in a selected academic discipline with exposure to a broad range of social science perspectives. Completion of the IDS program adds the designation "International Development Studies”  to the MASc degree. This designation gives extra flexibility in the job market while permitting disciplinary specialization required by most PhD programs.

Degree Requirements

Students complete International Development Studies core requirements and the requirements of their home department.

IDS Master’s Core requirements:

  • IDEV*6200 [1.00] Development Theory, Issues and Process
  • IDEV*6300 [0.50] Research and Analysis in a Development Context

Optional IDS Courses:

  • IDEV*6000 [0.50] Regional Context
  • IDEV*6500 [0.50] Fieldwork in International Development Studies

Requirements from the School of Engineering:

  • Three courses from the list of required graduate courses in Engineering (to be selected in consultation with Advisor)
  • Thesis


Collaborative Specialization in Artificial Intelligence ( MASc.)

Types of Careers

Students interested in Artificial Intelligence (AI) related research and Thesis work can choose to combine their MASc with a specialization in AI (MASc.ENGG + AI). The AI collaborative specialization in AI offers students a diverse and comprehensive knowledge base in AI. Students learn from a multidisciplinary team of faculty with expertise in fundamental and applied deep learning and machine learning, while conducting AI-related research guided by a faculty supervisor. Through a combination of online learning, lectures, team-based problem-solving and experiential learning opportunities, students obtain broad expertise in machine learning and AI, including essential skills in programming and algorithmic thinking, mathematical foundations and statistical analysis for AI, optimization, and data visualization. Students also develop an intimate understanding about the policy, regulatory and ethical issues related to AI and its uses.
Graduates will be in-demand by government and private industry, for positions that include: machine learning researcher, computer vision engineer, data engineer, data scientist, software engineer, statistician.

Affiliated Faculty 

Students interested in AI are encouraged to reach to one of our AI affiliated faculty members to see if their research interest align and if the faculty member is currently accepting new students. Visit our list of AI Affiliated Faculty.

Degree Requirements

Please see the Collaborative Specialization in Artificial Intelligence website for more information and the CSAI Course List.

Masters students in the collaborative specialization in artificial intelligence must complete 2.25 credits total including:

  • UNIV*6080 Computational Thinking for Artificial Intelligence [0.25]
  • UNIV*6090 Artificial Intelligence and Society [0.50]

0.50 credits from the following Elective Core courses:

  • CIS*6020 Artificial Intelligence [0.50]
  • ​ENGG*6500 Introduction to Machine Learning [0.50]​
  • ENGG*6600 ST: Machine Learning Applications [0.50]
  • STAT*6801 Statistical Learning [0.50]

1.0 credits from the following Complementary AI-related courses:

  • BINF*6970 Statistical Bioinformatics [0.50]
  • CIS*6050 Neural Networks [0.50]
  • CIS*6060 Bioinformatics [0.50]
  • CIS*6070 Discrete Optimization [0.50]
  • CIS*6080 Genetic Algorithms [0.50]
  • CIS*6120 Uncertainty Reasoning in Knowledge Representation [0.50]
  • CIS*6160 Multi-agent Systems [0.50]
  • CIS*6170 Human-Computer Interaction [0.50]
  • CIS*6180/DATA*6300 Analysis of Big Data [0.50]
  • CIS*6190/DATA*6400 Machine Learning for Sequential Data Processing [0.50]
  • CIS*6320 Image Processing Algorithms and Applications [0.50]
  • CIS*6420 Soft Computing [0.50]
  • ENGG*4460 Robotic Systems [0.50]
  • ENGG*6090 Image Analysis [0.50]
  • ENGG*6100 Machine Vision [0.50]
  • ENGG*6140 Optimization Techniques for Engineering [0.50]
  • ENGG*6570 Advanced Soft Computing [0.50]
  • ENGG*6600 ST: Reinforcement Learning *Fall 2022 offering only
  • MATH*6020 Scientific Computing [0.50]
  • MATH*6021 Optimization I [0.50]
  • MATH*6051 Mathematical Modelling [0.50]
  • PHIL*6400 Ethics of Data Science (formerly PHIL*6760 Science and Ethics) [0.50]
  • STAT*4000 Statistical Computing [0.50]
  • STAT*6721 Stochastic Modelling [0.50]
  • STAT*6821 Multivariate Analysis [0.50]
  • STAT*6841 Computational Statistical Inference [0.50]
  • ENGG*6600 ST: Information Retrieval 
  • CIS*6650 ST: Machine Learning
  • CIS*6650 ST: Data Integration 
Note: at least 1.0 credits must be from ENGG graduate courses; CSAI students can elect to take a second "elective core course" in lieu of a complementary AI-related course.

We are a Vector Institute-Recognized Masters Program

Students who are enrolled in the University of Guelph Collaborative Specialization in Artificial Intelligence are eligible to apply for Vector Scholarships in Artificial Intelligence, valued at $17,500 each. You can find more information at the Vector Institute website.