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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 therefore 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, 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, indepenent 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.
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?
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.
TU Delft (Delft University of Technology)
Working at TU Delft means contributing to solutions that really make a difference.
For over 180 years, we have been training engineers who make an impact worldwide in companies, government bodies, or as entrepreneurs. Our alumni turn knowledge into concrete solutions for the challenges of today and tomorrow. These challenges are changing rapidly. That is why we focus on themes such as energy, climate, digitalisation, artificial intelligence (AI), and smart mobility every day. Our education and research are directly aligned with what society needs now and in the future.
At TU Delft, our people make the difference. With their knowledge and curiosity, our staff provide a 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 join an international community of professionals and students. Together, we 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. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Our research projects are conducted in close cooperation with a wide range of research institutions. CEG is convinced of the importance of open science and supports its scientists in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management.
& Geosciences.
Conditions of employment
Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with 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.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from €3059 - €3881 gross per month, from the first year to the fourth year based on a fulltime contract (38 hours), plus 8% holiday allowance and an end-of-year bonus of 8.3%.
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.
The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.
Will you need to relocate to the Netherlands for this job? TU Delft is committed to make your move as smooth as possible! The HR unit, Coming to Delft Service, offers information on their website to help you prepare your relocation. In addition, Coming to Delft Service organises events to help you settle in the Netherlands, and expand your (social) network in Delft. A Dual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.
Additional information
For more information about this vacancy, please contact Hans van Lint
[email protected] or Serge Hoogendoorn
[email protected].
Application procedure
Are you interested in this vacancy? Please apply no later than 2 August 2026 via the application button.
Please submit the following documents:
A motivation letter.
Curriculum Vitae.
Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.
Please note:
You can apply online. We will not process applications sent by email and/or post.
As part of knowledge security, TU Delft conducts a risk assessment during the recruitment of personnel. We do this, among other things, to prevent the unwanted transfer of sensitive knowledge and technology. The assessment is based on information provided by the candidates themselves, such as their motivation letter and CV, and takes place at the final stages of the selection process. When the outcome of the assessment is negative, the candidate will be informed. The processing of personal data in the context of the risk assessment is carried out on the legal basis of the GDPR: performing a public task in the public interest. You can find more information about this assessment on our website about knowledge security.
Please do not contact us for unsolicited services.
Faculty/Department: Faculty of Civil Engineering and Geosciences
Salary range: €3059 - €3881
Submission is possible until: 2 Aug 2026
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