PhD Position Design and Control of Mixed Fixed-Flexible Transport Networks

PhD Position Design and Control of Mixed Fixed-Flexible Transport Networks

Would you like to work at the intersection of transportation, robotics and machine learning to design 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 the FlexMobility project 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. In the future, these mixed transportation systems may include a fleet of autonomous cars, vans, and buses.

This PhD position within FlexMobility will focus on the underlying assignment and routing algorithms for real-time operation of the vehicle fleet and the multi-objective design of the mixed transportation network. Our key hypothesis is that it is possible to design a mixed network by simulating how to serve a given demand with an on-demand ride?'pooling service, tracking the vehicles' routes, and allocating fixed lines wherever vehicles concentrated the most. For the implementation of the system, users will be allocated, in real time, to either the fixed lines or pooled on-demand vehicles. This requires efficient methods for large scale task assignment and routing leveraging combinatorial optimization and machine learning. To achieve a holistic system, the developed methods will be enhanced with behavioral representations researched by another PhD candidate in the project. The developed methods could be applicable across many multi?'agent coordination domains, from mobility, to logistics and multi?'robot systems.

In this work, we will consider two use cases: (1) a mobility network considering both fixed?'line buses and on?'demand vehicles, and (2) a network with water?'taxis. 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.

https://autonomousrobots.nl/.

Job requirements

We are looking for a candidate with operations research, discrete planning, robotics or machine learning knowledge. As the project is a multi?'faceted one, we expect candidates with an appreciation of the interaction between operations research and machine learning.

• Master of Science (MSc) diploma in Transportation, Robotics, Computer Science, Logistics, Operations Research, Industrial Engineering, Applied Maths or any other related field.
• Drive for excellence in research and the ability to work independently and as part of a team.
• Willingness to conduct multidisciplinary research in collaboration with both scientific and industrial partners.
• Excellent problem?'solving and analytical skills.
• 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 a 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.

Click here to go to the website of the Faculty of Mechanical Engineering. Do you want to experience working at our faculty? These videos will introduce you to some of our researchers and their work.

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.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

Coming to Delft ServiceDual Career Programme is available, to support your accompanying partner with their job search in the Netherlands.

Additional information Application procedure

• A motivation letter (max 1 page) explaining your motivation and ambitions related to the PhD position and your relevant skills.
• Your CV.
• Name and contact details of at least two referees (preferably including your MSc thesis supervisor).
• Transcripts of your MSc and BSc studies.
• Your MSc Thesis and publication list (if applicable; you may apply prior to completing the MSc thesis).

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:

• 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.