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

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.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.

Postdoc in Physics‑Informed Machine Learning for Hybrid Traffic Prediction

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. 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 and 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 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 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.
• 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.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.

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

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.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.

Postdoc in Physics‑Informed Machine Learning for Hybrid Traffic Prediction

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. 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 and 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 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 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.
• 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.

Click here to go to the website of the Faculty of Civil Engineering & Geosciences.