2 PhD positions in Physics‑Informed Machine Learning for Traffic Modelling & Prediction

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

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. We believe traffic management based on reliable predictions is crucial to ensure accessibility and safety, especially during major events, accidents and extreme weather.

In a new project called deepTraffic, funded by the Dutch science foundation NWO, we aim to develop a new generation of traffic prediction methods by combining traffic flow theory with machine learning: theory and logic where necessary, data-driven where possible. This innovative 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 our team. We have 2 PhD and one postdoc positions, all supervised by a highly experienced team of four researchers supported by a technician. You will work in a highly collaborative team where your ideas matter from day one, independent thinking is encouraged, and you will get the support you need to further develop your scientific career.

PhD Position 1 - Hybrid Traffic Flow Modelling

This PhD focuses on developing hybrid traffic flow models that combine physical modelling principles with machine learning approaches, such as Physics-Informed Neural Networks (PINNs) and machine-learning-enhanced traffic models.

You will:

• Develop next-generation hybrid traffic flow models that combine traffic theory with machine learning.
• Investigate Physics-Informed Neural Networks (PINNs) and related approaches for network-wide traffic prediction.
• Design physically consistent and interpretable machine-learning methods for dynamic traffic systems.
• Test and validate prediction models using large-scale real-world traffic data from Dutch freeway networks.

PhD Position 2 - Data Assimilation and Network State Estimation

This PhD focuses on estimating key traffic states and inputs, such as path flows, boundary conditions, and other dynamic network variables.

You will:

• Develop new data assimilation methods for estimating traffic states and network conditions.
• Combine machine learning with traffic flow theory to improve prediction reliability and robustness.
• Estimate path flows, boundary conditions, and other key inputs for large-scale traffic models.
• Design scalable methods for real-time traffic prediction and uncertainty quantification in operational networks.

The connection with practice is super important. This project is not just an academic exercise. We will work closely together 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.

Herein, 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 an Msc degree in a STEM 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 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, our people make the difference. With their knowledge and curiosity, our 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.

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. Our research feeds into our educational programmes and covers 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 its scientists in integrating open science into their research practice.

Conditions of employment

Doctoral candidates will be offered a 4-year period of employment in principle, in the form of 2 employment contracts. An initial 1.5 year contract includes an official go/no go progress assessment within 15 months, followed by an additional contract for the remaining 2.5 years assuming everything goes well and performance requirements are met.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.