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
Organizational Unit:
Automotive and Surface Transportation
Classification:
RO
Tenure:
Term
Duration:
2 years
Language Requirements:
English
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 programThe NRC's Postdoctoral Fellowship program offers PDFs access to unique world-class facilities and the opportunity to work alongside multi-disciplinary teams of expert researchers and technicians on projects of critical importance to Canada. PDFs will carry out research on innovative research projects, with opportunities for career development (publications and/or industry interaction).
PDFs will be offered appointments to the staff of the National Research Council on a term basis and will be offered salaries and perks currently available to Research Officers. Salaries commensurate with experience are taxable and subject to other deductions. As a guide, the current annual PhD recruiting rate is $83,985. In addition PDFs will be allocated a travel fund to support their work.
Project titleLearning Driver Attention Strategies in Adverse Weather Using Graph-Based Inverse Reinforcement Learning.
The roleThe 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.
EducationPhD in Computer Engineering, Computer Science, or a related field.
For information on certificates and diplomas issued abroad, please see Degree equivalency
Experience
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 clear 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.
Language requirementsEnglish
Information on language requirements and self-assessment tests
Condition of employmentReliability 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 re

📌 Postdoctoral Fellow, Reinforcement Learning And Vision (Canada)
🏢 Performance and Learning
📍 Canada