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.