Postdoctoral Fellow, Reinforcement learning and vision

National Research Council of Canada; Hamilton; On-site; CAD 84,000 - 117,000; Full time

Benefits offered by this job

Pension plan

Health & dental coverage

Job summary

The National Research Council of Canada (NRC) in Hamilton, ON, invites applications for a Postdoctoral Fellow in reinforcement learning and vision. You will develop a graph-based IRL framework to analyze driver gaze and forecast future trajectories, integrating spectral graph analysis and deep learning for prediction in adverse weather.

You will join a multi-disciplinary team, with opportunities to publish and contribute to high-impact research, while completing two-year terms with strong

Qualifications

• PhD in Computer Engineering, Computer Science, or related field.
• Two to six years of research experience preferred.
• Strong programming skills in Python or C++.
• Experience with deep learning frameworks (PyTorch, TensorFlow).
• Proven ability to publish in top venues is desirable.

Responsibilities

• Develop a graph-based IRL framework to analyze eye-gaze data.
• Apply spectral graph analysis to capture gaze pattern shifts.
• Incorporate deep learning models to forecast gaze trajectories and driving actions.
• Collaborate with a multi-disciplinary team on related projects.

Skills

Graph-based IRL

Deep learning

Python

C++

Spectral graph analysis

Education

PhD in Computer Engineering or Computer Science

Tools

PyTorch

TensorFlow

OpenCV

Scikit-learn

NumPy

Pandas

Job description

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 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.

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 attachments».

In addition, applicants who best meet the requirements of the position will be asked to provide three letters of recommendation at a later stage of the competition process.

Compensation

The intent of this hiring action is to staff the position at the AsRO level, which is an early-career level position with a salary range of $83,985 to $116,641. This wide range accounts for varying degrees of expertise and impact, and growth within the role.

Salary determination will be based on a thorough evaluation of the successful candidate's prior experience. This evaluation will consider specific achievements, skills, and contributions that demonstrate the candidate's impact and expertise. As a guide, the average starting rate for new PhD graduates entering this role is $83,985.

The NRC is committed to supporting career progression within the organization, providing access to the opportunities and resources needed for a pathway to a fulfilling career. NRC employees also enjoy a wide-range of competitive benefits including a robust pension plan, comprehensive health and dental coverage, disability and life insurance, office closure at the end of December, and additional supports to enhance your well-being throughout your career and beyond.

Discover the possible

Anything is possible at the NRC, named in 2025 one of Canada’s Top Employers for Young People, Top Employer in the National Capital Region and Forbes Canada’s Best Employers!

As Canada’s leading research and technology organization, our world-renowned research pushes the boundaries of science and engineering to make the impossible, possible. Every day we explore new ideas through innovative research and help companies discover possibilities that impact Canada’s future and the world.

At the NRC, you’ll also discover new possibilities. Our supportive workplace f