PhD Position in OR-AI for Dynamic and Stochastic Integrated Transport Optimization

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%.

Your profile

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.

Our offer

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.

Information and application

Are you interested in being part of our team? Please submit your application before November 26th and include:

A cover letter (maximum 2 pages A4), emphasizing your specific interest, qualifications, and motivations to apply for this position;

A Curriculum Vitae, including a list of all courses attended and a transcript of all grades obtained (BSc/MSc), and, if applicable, a list of publications and references;

An IELTS-test, Internet TOEFL test (TOEFL-iBT), or a Cambridge CAE-C (CPE). Applicants with a non-Dutch qualification and who have not had secondary and tertiary education in English can only be admitted with an IELTS test showing a total band score of at least 7, internet. TOEFL test (TOEFL-iBT) showing a score of at least 90, or a Cambridge CAE-C (CPE).

Additional information can be acquired via email from [email protected].

About the department

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.

About the organisation

The Faculty of Behavioral, Management and Social sciences (BMS) aims to play a key role in understanding, jointly developing and evaluating innovations in society. Technological developments are the engine of innovation. As a technical university that puts people first, we tailor them to human needs and behavior and use social engineering to integrate them into society. We also ensure adequate governance at public and private level, and robust, inclusive and fair organizational structures. We do this by developing, sharing and applying high-quality knowledge in Psychology, Business Administration, Public Administration, Communication Sciences, Philosophy, Educational Sciences and Health Sciences. Our research and education in these disciplines revolves around tackling and solving societal challenges. The research programs of BMS are closely linked to the research of the UT institutes Mesa+ Institute for Nanotechnology, TechMed Center and Digital Society Institute.

As an employer, the Faculty of BMS offers work that matters. We equip you to create new possibilities for yourself and for our society. With us, you will become part of a leading technical university with increasing, positive social impact. We offer an open, inclusive and entrepreneurial atmosphere, in which we encourage you to make healthy choices, for example through our flexible, adaptable benefits.

Want to know more? Alves Beirigo, B. (Breno)

Assistant Professor of Stochastic Operations Research

Alves Beirigo, B. (Breno)

Assistant Professor of Stochastic Operations Research

Do you have questions about this vacancy? Then you can contact Breno for all substantive questions about this position and the application procedure. For general questions about working for the UT, please refer to the chatbot.

Contact

Phone:+31534896719

Email:[email protected]

How to apply Step 1

Apply. When you see a vacancy that appeals to you, you can apply online. We ask you to upload a CV and motivation letter and/or list of publications. You will receive a confirmation of receipt by e-mail.

Step 2

Selection. The selection committee will review your application and you will receive a response within 2 weeks after the vacancy has been closed.

Step 3

1st interview. The 1st (online or in person) meeting serves as an introduction where we introduce ourselves to you and you to us. You may be asked to give a short presentation. This will be further explained in the invitation.

Step 4

2nd interview. In the second interview, we will further discuss the job content, your skills and your talents.

Step 5

The offer. If the conversations are positive, you will be made a suitable offer.

Your Colleagues

Mes, M.R.K. (Martijn)

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