Date Posted: 02/27/2026

Req ID: 47098

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 70 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.

The Department of Mechanical & Industrial Engineering prides itself on being a good place to work: respectful, accepting of difference and professional. We value employees who are goal-oriented and self-motivated, and who are constantly looking to improve how we serve our students and faculty. We believe in teamwork, in working together for the greater good, and yet doing so in a way that respects each employee’s work-life balance.

Finally, we value each employee as an individual, and look for opportunities to recognize each contribution.

Your Opportunity The Research Associate (Term) will work under the co-supervision of Prof. Scott Sanner (MIE, St. George) and Prof. Florian Skhurti (Computer Science, UTM) to maintain and develop learning and planning methodologies for world models in the PyRDDLGym software ecosystem.

The Research Associate (Term) Position Will Be Responsible For

• Develop and execute research vision with PI to achieve research goals.
• Conducting novel, publishable research related to machine learning, deep learning, reinforcement learning, and planning.
• Development and maintenance of Python software and packages.
• Drafting of documentation to support the dissemination and release of software.
• Design, implementation, and evaluation of experiments.
• Drafting of publication submissions documenting research innovations and outcomes.
• Communicating with project stakeholders in industry and academia.
• Managing and leading regular research meetings.
• Developing research proposals and providing support with grant applications where appropriate.
• Preparing presentations where appropriate.
• Provide technical support to related R&D projects in the areas of machine learning and AI.
• The Research Associate is expected to publish both at conferences and in journals.

Qualifications Education

• PhD degree in a STEM-related field (e.g., engineering, mathematics, computer science, or operations research) with demonstrated expertise in machine learning, deep learning, reinforcement learning, planning and related AI related topics.

Experience

• Minimum two years of research experience in academia at the post-doctoral level on machine learning and AI-related topics.
• Experience in deep learning.
• Experience in planning and reinforcement learning.
• Experience working with robotics platforms.
• Experience in mathematical presentation, derivation, and proofs of theorems related to machine learning and AI.
• Experience developing, maintaining, and documenting Python software packages.
• Experience maintaining large GitHub repositories of software and addressing user issues in a timely manner.
• Cross-sector experience spanning private industry and research organizations will be seen as a significant advantage.
• Preference will be given to candidates with a strong record of creativity and innovation in AI, as demonstrated by their publication record.

Skills

• Strong technical and analytical skills with solid understanding of machine learning, deep learning, reinforcement learning, and planning.
• Proficient in Python programming.
• Proficient in the deep learning framework of JAX.
• Proficient in the optimization framework of Gurobi.
• Strong technical writing and research communication skills in both academic and industrial contexts.
• Capable of managing multi-institutional R&D projects and facilitating collaboration between industry and academia.
• Demonstrable ability to apply initiative, tact, judgement, accuracy, and confidentiality with meticulous attention to detail.
• Superior problem solving and interpersonal skills with a demonstrated positive attitude and service orientation towards students, staff and the public.
• Proven capability to work independently, with instruction, and within a team environment.
• Proven ability to organize, multi-task, manage conflicting priorities and meet all deadlines while quickly adapting and learning new processes.
• Demonstrated commitment to equity, diversity, inclusion and the promotions of a respectful and collegial learning and working environment.

Closing Date: 03/09/2026,11:59PM 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

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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 uoft.careers@utoronto.ca.

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