Would you like to investigate the underlying behavioral phenomenon for innovative designs of mixed fixed-flexible transport networks?
Job description
The increase of public transport usage has clear potential in transforming our environment to be more liveable, sustainable and convenient. However, to ensure economic viability with off-peak times and relatively remote locations, while increasing the attractiveness to the users, we need innovative designs where fixed and flexible services support each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies.
In FlexMobility we propose a holistic approach to design a public transport network that includes both traditional fixed lines and flexible on-demand services, while considering the underlying travel behaviour. To reach a user-centric public transport system we will also study how the demand would react to such a mixed system. We will do so by combining revealed preferences data on the already available systems together with stated preferences in tailored surveys for enriching the understanding of the mixed system. The behavioural understanding on the demand side will provide information to the supply side, such as the trade?'offs between different attributes of the system, or the acceptable walking and waiting times, which will be used both to adapt the design and to have more realistic simulations. Supply and demand levels are intimately intertwined. While the chosen design has a significant effect on the most important aspects of users' experience, the algorithms,
and thus the supply decisions, are based on users' preferences.
This PhD position within FlexMobility, will focus on the supply?'demand interactions to guide the design of mixed fixed?'flexible transport networks. This will start with the behavioral investigation based on the available data as well as the new survey data to be collected. The purpose of this first step will be to identify the key attributes of the transport system that influence the preferences of the users while accounting for the behavioral heterogeneity in the population. Methodologically, we might explore both random utility models as well as machine learning, potentially in a hybrid way, in onder to capture complex phenomenon. The behavioral understanding will guide the representation of supply?'demand interactions in the next step. A baseline methodology will be an iterative process between supply and demand models, which will then be expanded towards endogenous representation of behavioral models in the network design problem. This integration of supply?'demand interaction will build on optimization models of the transport network and will be enhanced with the behavioral representations. The decisions to be optimized could potentially exploit model?'based learning approaches.
In this work, we will consider two use cases: (1) bus network considering both fixed?'line buses and on?'demand ones (2) a network with water?'taxis which operate in fixed and flexible modes at different times of the day. For both use cases, there will be interaction with the project partners for generating/obtaining the needed data as well as for setting up realistic case studies.
The position is available as of October 1, 2025 with a flexible start date to be agreed upon. The PhD candidate will join the group of Bilge Atasoy, with various researchers working on adaptive transport and logistics systems through methodologies of dynamic and predictive optimization, behavioral modeling and machine learning. There is vivid interaction within the group to foster collaboration both with scientific and social activities. The PhD candidate will also interact with the group of Javier Alonso?'Mora as part of the FlexMobility project as there will be in total 2 PhDs and 1 postdoctoral researcher working in a collaborative way to reach the goal of designing user?'centric flexible transport networks. As part of the PhD position, there will be opportunities to gain teaching experience in the relevant courses and/or supervising MSc students.
Job requirements
We are looking for a candidate with transportation research knowledge who is comfortable with operations research methodologies as well as behavioral modeling. As the project is a multi?'faceted one, we expect candidates with an appreciation of the interaction between behavioral modeling, operations research and machine learning.
- Master of Science (MSc) diploma in Transportation, Logistics, Operations Research, Industrial Engineering, Applied Maths or any other related field.
- Attitude to function both in a team and independently.
- Willingness to conduct multidisciplinary research in collaboration with both scientific and industrial partners.
- Drive for excellence in research.
- TOEFL or IELTS English proficiency tests for all applicants except those graduated from an MSc program that was taught in English. The minimum requirement of an TOEFL score of 100 IELTS of 7.0 per sub?'skill (writing, reading, listening, speaking).
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 Mechanical Engineering
From chip to ship. From machine to human being. From idea to solution. Driven by a deep?'rooted desire to understand our environment and discover its underlying mechanisms, research and education at the ME faculty focusses on fundamental understanding, design, production including application and product improvement, materials, processes and (mechanical) systems.
ME is a dynamic and innovative faculty with high?'tech lab facilities and international reach. It's a large faculty but also versatile, so we can often make unique connections by combining different disciplines. This is reflected in ME's outstanding, state?'of?'the?'art education, which trains students to become responsible and socially engaged engineers and scientists. We translate our knowledge and insights into solutions to societal issues, contributing to a sustainable society and to the development of prosperity and well?'being. That is what unites us in pioneering research, inspiring education and (inter)national cooperation.
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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 Promovendus gross per month, from the first year to the fourth year based on a full?'time 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.
Coming to Delft ServiceDual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.
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Are you interested in this vacancy? Please apply no later than 15 November 2025 via the application button and upload the following documents:
- A motivation letter (max 1 page) explaining your motivation and ambitions related to the PhD position and your relevant skills.
- Your CV.
- Your MSc Thesis and publications (if applicable).
- Name and contact details of at least two referees (preferably including your MSc thesis supervisor).
- Transcripts of your MSc and BSc studies.
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.
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