PhD in Physics‑Informed ML Traffic Prediction

Position: PhD in Physics‑Informed ML for Traffic 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 therefore crucial to ensure accessibility and safety, especially during major events, accidents and extreme weather. In a new project called deep Traffic (funded by the Dutch science foundation NWO), we aim to develop a new generation of traffic prediction methods, combining traffic flow theory with machine learning, and with that, the best of both worlds: theory and logic where necessary, data‑driven where possible.

This innovative new approach enables more efficient and robust management of large traffic networks under all conditions.

You have the most important role in this ambitious project as one of the young talents in our team. We have 2 PhD and one postdoc positions, all of whom will be supervised by a highly experienced team of four (top) researchers in this field 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 all 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.

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.

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 makes 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?

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.

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.

Conditions of Employment
Doctoral candidates will be offered a 4‑year…