University of Toronto
10 months ago
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Postdoctoral Fellowship in World Models and Reinforcement Learning for Traffic Control at University of Toronto University of Toronto in Canada
Degree Level
Postdoc
Field of study
Computer Science
Funding
The position is a postdoctoral fellowship. No explicit details on stipend amount, tuition, or funding type are provided.
Deadline
Expired
Country
Canada
University
University of Toronto
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Keywords
About this position
A postdoctoral fellowship is available at the University of Toronto, focusing on world models and reinforcement learning (RL) for traffic signal control. This opportunity is ideal for candidates with a strong publication record in top-tier machine learning and RL venues, and with experience in traffic systems. The research will involve developing and applying advanced AI and machine learning techniques to optimize traffic control, with the goal of improving urban mobility and efficiency.
Responsibilities include publishing research in leading venues, producing high-quality code for research partners, and providing research supervision. The successful candidate will join a vibrant research environment at the University of Toronto, collaborating with experts in machine learning, artificial intelligence, and traffic systems. The position is supervised by Professor Scott Sanner, a recognized leader in the field.
The application deadline is early February 2026, with the fellowship expected to commence no later than September 2026. To apply, candidates should email Professor Sanner with a CV, a link to their public GitHub account, and a brief statement of interest. While the post does not specify funding details, it is a postdoctoral fellowship at a leading Canadian institution, offering an excellent platform for research and career development in AI, RL, and traffic control systems.
Keywords: reinforcement learning, world models, traffic signal control, machine learning, artificial intelligence, postdoctoral fellowship, University of Toronto.
Funding details
The position is a postdoctoral fellowship. No explicit details on stipend amount, tuition, or funding type are provided.
What's required
Applicants must have a strong publication record in top-tier machine learning and reinforcement learning venues, with ideal experience in traffic systems. Candidates should be able to publish in leading research venues, produce high-quality code for research partners, and provide research supervision. No specific degree or language requirements are mentioned, but a PhD in a relevant field is implied.
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Professors
Professor
Canada
Research Interests
Internet Of Things
Artificial Intelligence
Energy Consumption
Information Technology
Inpatient Care
ApplyKite TwinLearns as you applyMake your next move.
Prepare your application
Review drafts and confirm requirements with the university.