Postdoc in Physics‑Informed Machine Learning for Hybrid Traffic Prediction

Hey machine learning enthusiast with a love for physics and complex systems, will you help develop a new generation of road traffic prediction methods at TU Delft?

Job description

Road traffic is a highly complex dynamic system. Minor disruptions can lead to major delays, with traffic jams spreading like oil spills over entire networks. Reliable predictions are crucial to ensure accessibility and safety, especially during major events, accidents and extreme weather.

In the new project deepTraffic, funded by the Dutch science foundation NWO, the aim is to develop a new generation of traffic prediction methods by combining traffic flow theory with machine learning, bringing together theory and logic where necessary, and data-driven methods where possible. This approach enables more efficient and robust management of large traffic networks under all conditions.

You will play an important role in this ambitious project as one of the young talents in the team. The project includes 2 PhD positions and 1 postdoc position, supervised by a highly experienced team of four researchers supported by a technician. PhD1 focuses on hybrid traffic flow modelling such as Physics inspired Neural Nets (PiNNs) or ML-inspired traffic models. PhD2 focuses on data assimilation and estimating start and boundary conditions such as path-flows, and other key parameters and inputs.

In your role as a postdoctoral researcher, you will:

• Integrate hybrid traffic models and data assimilation methods into a coherent prediction framework.
• Develop uncertainty quantification methods and explainable, trustworthy AI approaches.
• Design visualisation to support decision-making by traffic operators and strategic advisors.
• Collaborate closely with road authorities, traffic management centres, and industry partners to test and validate methods in real-world use cases.
• Mentor the PhD candidates while shaping the scientific direction and integration of the project.

The connection with practice is essential. This project is not just an academic exercise. The team will work closely with road authorities, traffic management centers, and industry to implement these prediction methods and test them against real constraints, with real data in real use cases on the Dutch freeway network. Explainability and trustworthiness are key: traffic management using predictions may render those very same predictions invalid. Predictions need to come with confidence bounds and a narrative that make them usable in decision-support systems for operators and strategic advisors.

Job requirements

We look for highly motivated, collaborative and creative candidates. Do you recognize yourself in many of these requirements?

Need to have:

• You hold a PhD in Transport Engineering, Civil Engineering, Computer Science, Data Science, Applied Mathematics, or a closely related quantitative field.
• You love physics and complex systems and are either familiar with, or very eager to learn about, road network traffic flow theory and simulation.
• You are interested in mentoring and supporting MSc and PhD students.
• You are a machine learning enthusiast and realist.
• You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#.
• You can present and communicate your ideas with and without LLMs.

Nice to have:

• You get excited about implementing your ideas.
• You are a team player: you enjoy sharing ideas and solving puzzles together.
• You also enjoy digging in and solving puzzles independently.
• You believe in, and want to contribute to, an inclusive, open and safe workspace.

TU Delft (Delft University of Technology)

Working at TU Delft means contributing to solutions that really make a difference.

At TU Delft, people make the difference. With their knowledge and curiosity, staff provide high-quality education and conduct pioneering research that extends beyond the campus. You will have the opportunity to take the initiative, work with others, and grow as a professional. Working at TU Delft means joining an international community of professionals and students. Together, they create knowledge, innovations, and solutions that help move the world forward.

Faculty of Civil Engineering and Geosciences

The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Its research feeds into educational programmes and addresses societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Research projects are conducted in close cooperation with a wide range of research institutions. CEG supports scientists in integrating open science into their research practice.

Conditions of employment

• Duration of contract is 4 years.
• An excellent pension scheme via the ABP.
• The possibility to compile an individual employment package every year.
• Discount with health insurers on supplemental packages.
• Flexible working week.
• Every year, 232 leave hours (at 38 hours). You can also sell or buy additional leave hours via the individual choice budget.
• Plenty of opportunities for education, training and courses.
• Partially paid parental leave.
• Attention for working healthy and energetically with the vitality program.