Cities’ true transportation potential is buried under a lack of transparency, coordination, and collaboration. Public-, company-, and individually-owned transportation resources (e.g., cars, boats, trucks, vans) and networks (e.g., streets, waterways, hubs) are not effectively orchestrated to achieve maximum satisfaction across transportation stakeholders, namely, cities, providers, and customers. As automation and digitalization increasingly take place, however, urban transportation can be envisioned as a smart-contract-mediated machine-to-machine interaction whose end goal is to seamlessly move people and goods via diverse transportation resources through the transport networks.

In this evolving context, this PhD project aims to design optimization- and learning-based strategies to manage a multi-stakeholder transportation ecosystem, which aims to achieve maximum individual and systemic benefits by simultaneously addressing the full range of needs of all its participants.

During the project, the PhD candidate will perform research on some of the following tasks:

• Solving High-Dimensional Optimization Problems: Develop innovative methods to address high-dimensional optimization problems commonly encountered in dynamic and stochastic transportation systems.
• Integrating AI and OR: Develop optimization- and learning-based strategies that align the interests and objectives of multiple stakeholders.
• Enhancing Transparency and Coordination: Develop methods to foster transparency, coordination, and collaboration for the optimal movement of people and goods across diverse transportation resources.
• Crafting Collaborative Policies: Propose policies that facilitate stakeholder collaboration and support the efficient utilization of transportation infrastructure, considering the diverse interests at play.
• Incorporating City-Centric Objectives: Explore approaches to incorporate the unique needs and objectives of cities into transportation optimization such as promoting equitable access, livability, and sustainability.

As a doctoral student, your key responsibilities will be to manage and carry out your research project within 48 months and write a PhD thesis. You will participate in research and training activities and disseminate research in the scientific community (international conferences) and non-scientific community by outreach and public engagement.

Furthermore, you will write articles for scientific peer-reviewed journals and progress reports and prepare results for publication and dissemination via public lectures, presentations, and the web.

You will also be responsible for taking courses per the research education and liaising with the other research staff, students, and partner institutions working in broad areas relevant to the research project.

Participation in other department duties, such as meetings, teaching, etc., is expected to cover a maximum scope of 20%.

• A background in operations research / mathematical optimization, preferably combined with experience in simulation, data science, and reinforcement learning.
• You hold, or will shortly acquire, a master’s degree in Computer Science, Industrial/Transportation Engineering, (Applied) Mathematics, Business Analytics, or a related discipline.
• Motivation to address transportation challenges (prior experience is a plus).
• Demonstrated programming skills in Python, Java, and C++ or similar languages.
• Capable of conducting independent research and willing to develop writing and publication skills.
• Strong ability to collaborate with stakeholders from academia and industry.
• Proficient in written and oral English (C1; above IELTS 7 or equivalent).
• A positive team spirit and enthusiasm for working in an interdisciplinary and internationally oriented environment.

We encourage high responsibility and independence while collaborating with colleagues, researchers, other university staff and partners. We follow the terms of employment by the Dutch Collective Labour Agreement for Universities (CAO). Our offer contains: a full-time 4-year PhD position with a qualifier in the first year; excellent mentorship in a stimulating research environment with excellent facilities; and a personal development program within the Twente Graduate School. It also includes:

• Gross monthly salary of € 2.770 in the first year, increasing each year up to € 3.539 in the fourth year;
• Excellent benefits including a holiday allowance of 8% of the gross annual salary, an end-of-year bonus of 8.3%, and a solid pension scheme;
• 29 holidays per year in case of full-time employment;
• A training programme as part of the Twente Graduate School where you and your supervisors will determine a plan for a suitable education and supervision;
• A green campus with free access to sports facilities and an international scientific community;
• A family-friendly institution that offers parental leave (both paid and unpaid);
• A full status as an employee at the UT, including pension, health care benefits and good secondary conditions are part of our collective labour agreement CAO-NU for Dutch universities.

The HBE cluster is dedicated to encouraging a supportive and inclusive working culture. Our aim is that all job applicants are given equal opportunities. When we select candidates for employment, it will be on the basis of their competence and ability. To support workforce diversity, we are open to offering flexible working conditions on an individual basis to support work-life balance, which may include a contract of employment, working hours and location, or childcare arrangements.

Please have a look at this video to learn about our wider HBE department:

The High Business Entrepreneurship corporate video can be watched via this link.