Research Associate - CARTE (12 Month Term)

University of Toronto; Toronto; On-site; CAD 54,000 - 100,000; Full time

Job summary

University of Toronto in Toronto, ON, invites applications for a Research Associate - CARTE (12 Month Term). The role focuses on applied AI/ML research within CARTE projects, with supervision by the Director of CARTE and collaboration across academic and industry partners.

The position emphasizes mentorship and publishing outcomes. The successful candidate will develop training materials, guide graduate students, and contribute to project deliverables while advancing research in machine

Qualifications

• Ph.D. degree in computer science, engineering, statistics, or related fields with emphasis on machine learning and artificial intelligence or acceptable combination of equivalent education and/or experience.

Responsibilities

• Develop and execute research vision with PI to achieve research goals.
• Conduct applied AI/ML research arising from CARTE’s industry-partnered projects and programs, prepare manuscripts for publication, prepare technical reports for partners, and contribute to research funding proposals led by CARTE faculty affiliates
• Provide technical leadership for the applied research projects undertaken with CARTE’s partner cohorts, acting as the main technical point of contact for each cohort from academic and technical onboarding through project completion, and coordinating the graduate student mentors who support individual project teams
• Provide day-to-day technical guidance and mentorship to graduate students and interns working on CARTE projects
• The research associate is expected to publish both at conferences and in journals
• Develop and maintain training materials for programming related to CARTE

Skills

Machine learning

Python

AI/ML research

Communication

Mentorship

Project leadership

Equity, diversity & inclusion

Education

Ph.D. in CS/Engineering/Statistics (ML/AI focus)

Tools

PyTorch

scikit-learn

Job description

Research Associate - CARTE (12 Month Term)

• Date Posted: 09/22/2026
• Req ID: 50281
• Faculty/Division: Faculty of Applied Science & Engineering
• Department: Dept of Mechanical & Industrial Eng
• Campus: St. George (Downtown Toronto)
• Existing Vacancy: Yes

Description

About Us

The Department of Mechanical & Industrial Engineering is home to about 60 professors leading research on a very broad range of topics. On the Industrial Engineering side, research areas include Operations Research, Information Engineering, Human Factors, and Applied Machine Learning, all of which seek to improve the systems we as humans rely on to navigate our world. On the Mechanical Engineering side, research areas include Robotics, Mechanics & Design, Materials, and Thermofluids, topics that are applied to applications including manufacturing, energy production, and bioengineering.

We are also home to about 2,000 very talented students: about 1,300 undergraduates are enrolled in the Industrial Engineering and in the Mechanical Engineering BASc programs. We have about 350 Master of Engineering (MEng) students, a professional program for students taking courses with us for a year or two. And we have about 350 MASc and PhD students, who work individually on research projects with a particular professor.

Your Opportunity

The Research Associate position will work under the supervision of the Director of CARTE to

• Develop and execute research vision with PI to achieve research goals.
• Conduct applied AI/ML research arising from CARTE’s industry-partnered projects and programs, prepare manuscripts for publication, prepare technical reports for partners, and contribute to research funding proposals led by CARTE faculty affiliates
• Provide technical leadership for the applied research projects undertaken with CARTE’s partner cohorts, acting as the main technical point of contact for each cohort from academic and technical onboarding through project completion, and coordinating the graduate student mentors who support individual project teams
• Provide day-to-day technical guidance and mentorship to graduate students and interns working on CARTE projects
• The research associate is expected to publish both at conferences and in journals
• Develop and maintain training materials for programming related to CARTE

Qualifications

Education

• Ph.D. degree in computer science, engineering, statistics, or related fields with emphasis on machine learning and artificial intelligence or acceptable combination of equivalent education and/or experience.

Experience

• Academic or professional experience of at least two (2) years in applied AI/ML, as a graduate student, postdoctoral fellow, researcher, or practitioner
• Experience with practical AI/ML projects implementable in the real world
• Experience in conducting collaborative research, presentations to technical audiences, and preparing manuscripts for academic publications
• Experience training students in academic or professional settings, including designing and delivering workshops, bootcamps
• Experience leading technical projects, including guiding students or junior team members
• Experience working with industry or external partners on applied projects preferred

Skills

• Deep understanding of machine learning paradigms and various algorithms, models, and validation techniques, including current large language model methods and tooling
• Proficiency in Python and the current machine learning ecosystem (for example, PyTorch and scikit-learn)
• Knowledge of the broader field of artificial intelligence (natural language processing, computer vision, optimization, and statistics)
• Ability to translate a partner’s problem statement into a scoped project or workshop and to judge what is technically feasible in the time available
• Excellent communication skills, both written and oral, including the ability to explain AI/ML methods to audiences with varied technical backgrounds
• Ability to initiate, refine, and complete projects with minimal guidance while contributing to several projects concurrently
• Strong technical and analytical skills with a record of research output, such as peer-reviewed publications, technical reports, or open-source contributions
• Demonstrated commitment to equity, diversity, inclusion and the promotion of a respectful and collegial learning and work environment.

To be successful in this role you will be:

• Eager to learn
• Resourceful
• Goal oriented
• Meticulous
• Problem solver
• Self-directed
• Comfortable working in a small team with a broad remit, including in-person delivery in Toronto

Closing Information

• Closing Date: 10/06/2026,11:59PM ET
• Employee Group: Research Associate
• Personnel Subarea: Research Assoc
• Appointment Type: Grant - Term
• Schedule: Full-Time
• Pay Scale Group & Hiring Zone: R01 -- Research Associates (Limited Term): $53,520 - $100,350
• Job Category: Engineering / Technical

Job Segment

Industrial Engineer, Mechanical Engineer, Engineer, Research Associate, Open Source, Engineering, Research, Technology

Diversity Statement

The University of Toronto embraces Diversity and is building a culture of belonging that increases our capacity to effectively address and serve the interests of our global community. We strongly encourage applications from Indigenous Peoples, Black and racialized persons, women, persons with disabilities, and people of diverse sexual and gender identities. We value applicants who have demonstrated a commitment to equity, diversity and inclusion and recognize that diverse perspectives, experiences, and expertise are essential to strengthening our academic mission.

As part of your application, you will be asked to complete a brief Diversity Survey. This survey is voluntary. Any information directly related to you is confidential and cannot be accessed by search committees or human resources staff. Results will be aggregated for institutional planning purposes. For more information, please see http://uoft.me/UP.

Accessibility Statement

The University strives to be an equitable and inclusive community, and proactively seeks to increase diversity among its community members. Our values regarding equity and diversity are linked with our unwavering commitment to excellence in the pursuit of our academic mission.

The University is committed to the principles of the Accessibility for Ontarians with Disabilities Act (AODA). As such, we strive to make our recruitment, assessment and selection processes as accessible as possible and provide accommodations as required for applicants with disabilities.

If you require any accommodations at any point during the application and hiring process, please contact [e-mail].