Select how often (in days) to receive an alert:

Postdoctoral Fellow, Reinforcement learning and vision

Priority may be given to the following designated employment equity groups: women, Indigenous Peoples* (First Nations, Inuit and Métis), persons with disabilities and racialized persons*.

* The Employment Equity Act, which is under review, uses the terminology Aboriginal peoples and visible minorities.

Candidates are asked to self-declare when applying to this hiring process.

City: Hamilton

OrganizationalUnit: Automotive and Surface Transportation

Classification: RO

Duration: 2 years

Work arrangements: Due to the nature of the work and operational requirements, this position will require full-time physical presence at the at the NRC work location identified.

The program

Learning Driver Attention Strategies in Adverse Weather Using Graph-Based Inverse Reinforcement Learning.

The role

The successful candidate will develop a graph-based inverse reinforcement learning (IRL) framework for analyzing and forecasting eye-gaze scanpaths of human drivers of motor vehicles. Our objective is to infer the underlying reward structures that govern gaze behavior and thereby reveal how attention strategies adapt to changing environmental conditions. By applying spectral graph analysis, we aim to derive quantifiable and interpretable metrics that capture shifts in gaze patterns across different weather scenarios. To extend this analysis toward prediction, we will incorporate deep learning model for forecasting future gaze trajectories and future driving action, yielding a complete pipeline from metric extraction to scanpath prediction. To the best of our knowledge,



this will be the first in-depth study of driver eye-gaze behavior under adverse weather, representing a novel contribution to the field.

In addition to the project described above, the successful candidate will be part of a multi-disciplinary team of researchers, and will be expected to participate in other projects to ensure a well-rounded post-doctoral training.

Education

PhD in Computer Engineering, Computer Science, or a related field.

- Significant* experience developing and applying advanced deep learning architectures, including transformer-based models, convolutional neural networks (CNNs), generative models, diffusion models, and deep reinforcement learning, to complex research and real-world applications.
- Experience in writing transparent and concise code in Python or C++.
- Experience using libraries and frameworks such as PyTorch, TensorFlow, Scikit-learn, NumPy, Pandas, and OpenCV to develop clean, efficient, maintainable, and well-documented code for collaborative research and production-level codebases.

The following experience criteria would be considered assets:

- Experience solving problems using graph theory.
- Experience with data collection and hardware setup including hardware development.
- Experience in human factors research.




- Experience publishing research in leading journals and presenting at conferences such as Journal of Machine Learning Research (JMLR), Transactions on Machine Learning Research (TMLR), NeurIPS, ICML, RLDM, ICLR, IROS, or other top IEEE venues.

* Significant experience is defined as two (2) to six (6) years of experience.

Reliability Status

For a Reliability Status, verification of background information over a period of 5 years is required.

Who is eligible?

- Fellowships will be for two years contingent on satisfactory progress achieved during the first year.
- Candidates should have obtained a PhD (or equivalent) within the past three years (PhD received on or after July 1, 2024*) or expect to complete their PhD within 6 months of appointment.
- Fellows will work in a lab under the direct supervision of an NRC researcher.

*Exceptions may be considered to recognize personal or family related leave. Please connect with us using the contact information below to discuss in more detail.

Application requirements

To be considered for the program please include the following in your application, please note that you will need to attach the required documents as per the list below when submitting your application. Failure to do so will result in your application being excluded.

- Resume
- Statement of Interest in the project (maximum one page in length)
- PhD Transcript - an electronic copy is sufficient, it does not have to be an official version.
- List of Publications

When submitting your application, you can include the required documents in any attachment field such as «Second language evaluation results» or «Other

📌 Postdoctoral Fellow, Reinforcement learning and vision (Canada)
🏢 National Research Council of Canada
📍 Canada