Postdoc in Developing Multi-Modal Bathtub Models with Autonomous Mobility-on-Demand

Help shape the next generation of urban traffic models by developing parsimonious, multi-modal frameworks for autonomous mobility-on-demand. Join TU Delft and develop advanced traffic models!

Job description
Emerging technologies have enabled new solutions and services for urban travel, including on-demand for-hire vehicles (e.g., Uber, Lyft) and connected and automated vehicles, which are increasingly part of the urban landscape. To design effective policy and management strategies, it is essential to understand how these emerging mobility systems interact with traditional transportation modes, such as buses and privately owned vehicles. This requires traffic models that can realistically describe multi-modal dynamics and support the evaluation of congestion, empty mileage, and the distribution of costs and externalities.

In this position, you will lead the development of multi-modal traffic flow models incorporating Autonomous Mobility-on-Demand (AMoD) services. The models must capture interactions between demand, congestion, and the dynamic operation of AMoD fleets, while remaining computationally efficient and generalisable. This will enable you to independently formulate and investigate novel research questions related to AMoD services.

Bathtub models, a.k.a. Macroscopic Fundamental Diagram (MFD) - based models, provide a powerful macroscopic framework for this purpose. These models abstract the whole network as a reservoir with a capacity that represents the maximal outflow. However, important gaps remain, particularly in representing realistic, mode-dependent trip distances, and departure time choices that evolve dynamically with congestion and AMoD operational strategies. To address these gaps, you will explore theory-driven mathematical extensions, potentially complemented by data-driven or machine-learning-based components where appropriate to extend bathtub models. Depending on background and interests, the resulting modelling framework may take the form of a system of partial differential equations (PDEs) - e.g., by extending the Generalized Bathtub Model to multi-modal systems - or an agent-based simulation model - e.g., by extending trip-based formulations of the bathtub model.

This research position is part of an ambitious project that aims to integrate within-day detours and day-to-day equilibration into multi-modal bathtub models. Within this broader effort, you will focus on extending the modelling framework to endogenously capture mode-dependent, time-varying trip distance distributions. You will collaborate closely with fellow researchers in the project who will analyse how demand and network characteristics shape shortest-path trip distance distributions, which you will then integrate and build upon within your modelling framework.

In parallel with model development, you will formulate and pursue well-defined research questions related to AMoD services, lead and contribute to scientific publications. Depending on interest, the position offers opportunities to supervise MSc students.

This position is embedded in the Transport & Planning department at TU Delft. Research at T&P is conducted across a multitude of relevant research themes, organized via collaborative labs. You will join the hEAT lab whose mission is to develop innovative and sustainable solutions to challenges involving electric and automated transport systems at different spatial and temporal scales. You will work in a collaborative and international team that encourages you to develop your skills, expand your network, and grow in your career.

Click here to learn more about T&P and here to know more about the hEAT Lab.

Job requirements
We are looking for a motivated and curious candidate who enjoys working on analytical and computational problems in an international research environment. You have:

Required:

• A PhD in Transportation Engineering, Civil Engineering, Applied Mathematics, Computer Science, or a closely related field.
• Solid background in traffic flow theory and/or choice modelling (e.g., logit models).
• Analytical skills and experience with numerical modelling and/or simulation.
• Good programming skills (e.g., Python).
• Fluent communication skills both spoken and written in English.

Nice-to-haves (one or more of the following):

• Experience in bathtub models, network fundamental diagrams, or macroscopic traffic flow theory.
• Experience with mode choice, departure time choice, or equilibrium modelling.
• Experience with agent-based models or PDE-based formulations.
• Experience with transport simulation tools (e.g. Aimsun, Omnitrans, or similar).
• Interest in or prior research experience with shared mobility, on-demand mobility, automated vehicles, and sustainable transport systems.

If you are interested in this topic but do not yet hold a PhD, you may also consider applying for the junior researcher position.

TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.

At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.

Challenge. Change. Impact!

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.

Conditions of employment

• Duration of contract is 13-15 months. Temporary.
• A job of 36-40 hours per week.
• Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
• 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.

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 Irene Martínez Josemaría (I.Martinez@tudelft.nl).

Application procedure
Are you interested in this vacancy? Please apply no later than 31 January 2026 via the application button and upload the following documents:

• CV including publication lists - max. 3 pages.
• Motivation letter describing your research interest and relevant experience for this position. - max. 1 page.
• The abstract of your PhD dissertation.
• A two-slide summary outlining your proposed approach to the research problem, highlighting your main objective, proposed key modelling steps, and potential challenges. The second slide should describe how your skills and experience enable you to address these challenges.
• Academic transcripts and diplomas from your MSc and PhD programmes.
• One selected publication.
• Contact details of two persons who can provide references.

You can address your application to Irene Martínez Josemaría.

We expect to hold job interviews in February 2026.

If your MSc and PhD diploma and transcript are not in Dutch, English, French or German and you will be the selected candidate, the TU Delft will ask you to deliver a certified translation in case you will be appointed.

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.