National Research Council Canada
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Postdoctoral Fellowship in Graph-Based Inverse Reinforcement Learning and Driver Vision Under Adverse Weather National Research Council Canada in Canada
Degree Level
Postdoc
Field of study
Computer Science
Funding
The two-year postdoctoral appointment offers a taxable salary and benefits at the AsRO level, with a salary range of $83,985 to $116,641. The guide rate for new PhD graduates is $83,985 annually. Benefits include a pension plan, health and dental coverage, disability and life insurance, and other employee supports. Fellows also receive a travel fund to support their work. Relocation assistance may be available according to NRC directives.
Deadline
Jan 7, 2027
Country
Canada
University
National Research Council Canada
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About this position
The National Research Council Canada is recruiting a Postdoctoral Fellow in reinforcement learning and vision for a two-year appointment in Hamilton, Canada, within the Automotive and Surface Transportation organizational unit.
The fellow will develop a graph-based inverse reinforcement learning framework to analyze and forecast human drivers’ eye-gaze scanpaths under adverse weather conditions. The project will infer reward structures underlying driver gaze behavior, use spectral graph analysis to quantify changes in attention strategies, and apply deep learning to predict future gaze trajectories and driving actions.
This postdoctoral research opportunity is suited to candidates in Computer Science, Computer Engineering, Robotics, computer vision, machine learning, or related fields. Required expertise includes deep learning architectures such as transformers, CNNs, generative and diffusion models, and deep reinforcement learning, together with Python or C++ programming and practical experience with PyTorch, TensorFlow, Scikit-learn, NumPy, Pandas, and OpenCV. Experience with graph theory, human factors, hardware, data collection, and high-impact publications is advantageous.
Applicants must have received a PhD on or after July 1, 2024, or expect to complete it within six months of appointment. The position offers a salary of $83,985–$116,641 annually, benefits, and a research travel fund. Applicants should submit a resume, one-page statement of interest, PhD transcript, and publication list by January 7, 2027.
Funding details
The two-year postdoctoral appointment offers a taxable salary and benefits at the AsRO level, with a salary range of $83,985 to $116,641. The guide rate for new PhD graduates is $83,985 annually. Benefits include a pension plan, health and dental coverage, disability and life insurance, and other employee supports. Fellows also receive a travel fund to support their work. Relocation assistance may be available according to NRC directives.
What's required
Applicants must hold a PhD in Computer Engineering, Computer Science, or a related field, obtained within the past three years (on or after July 1, 2024), or expect to complete the PhD within six months of appointment. Candidates need significant experience, defined as two to six years, developing and applying advanced deep learning architectures, including transformer-based models, convolutional neural networks, generative or diffusion models, and deep reinforcement learning. Experience writing clear, concise Python or C++ code and using PyTorch, TensorFlow, Scikit-learn, NumPy, Pandas, and OpenCV is required. Assets include graph theory, data collection and hardware setup, human factors research, and publications or presentations at leading machine learning, robotics, or IEEE venues. English is required, and successful candidates must obtain Reliability Status clearance. Applications must include a resume, a maximum one-page statement of interest, PhD transcript, and publication list; three recommendation letters may be requested later.
How to apply
Apply through the NRC recruitment portal using the direct application link. Attach a resume, one-page statement of interest, PhD transcript, and publication list, and include citizenship information. Questions may be directed to [email protected], citing requisition 24482.
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ApplyKite TwinLearns as you applyMake your next move.
Prepare your application
Review drafts and confirm requirements with the university.