PhD Position Adaptive Optimization and Learning Methods for Transportation Systems

15 Feb 2024
Job Information

Organisation/Company

Delft University of Technology (TU Delft)

Research Field

Technology

Researcher Profile

First Stage Researcher (R1)

Country

Netherlands

Application Deadline

29 Mar 2024 - 22:59 (UTC)

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

40.0

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

One of the biggest challenges for transportation systems is how to wisely utilize the available resources while responding to the demand. According to Eurostat, 20% of road freight kilometres in the EU in 2020 were driven by empty vehicles and this is similar for other modes of transportation. There are various reasons for the underutilization of transportation capacity. Firstly, there are uncertainties in the system, e.g., demand is fluctuating, the travel and service times vary significantly. In order to cope with that, operators frequently end up allocating more resources than needed. Secondly, transport systems have complex supply-demand interactions which makes it difficult to optimize the decisions on different resources. Underutilization of capacity entails costs that do not generate revenue and contribute to CO2-emissions, whereas the transportation sector is striving for sustainability goals. Being able to adapt the decisions – e.g., the network design, allocation of capacity, routing and scheduling - according to evolving demand and conditions in the transport network is a promising direction to improve the utilization of available resources.

ADAPT-OR project is funded by European Research Commission for Fundamental Research. The aim of ADAPT-OR is to develop self-learning capabilities towards adaptive transportation systems by leveraging the intersection of operations research, behavioural modelling and machine learning methodologies. The idea is to make use of information from the system itself across different decision-making levels, from the users and from the external environment in a self-learning manner in order to continuously adapt the decisions at different levels. For example, depending on the trends in behavior for a given delivery service, the network design can be adapted.

This particular PhD project focuses on bringing behavioral understanding into the adaptive optimation models within ADAP-OR. This entails that behavioral foundations need to be exploited for optimizing transport decisions. The representation of user behavior might strongly benefit from machine learning methodologies in order to have a self-learning capability also in terms of capturing the trends and evolutions in behavior. Integration of behavioral models brings in challenges for the tractability of the optimization models and model-based learning will be one of the solutions for this. The PhD student will be interacting with the other researchers working for the ADAPT-OR project in order to make use of the synergies as well as for the development of the case studies to showcase the benefits of the methodologies.

The start date of the PhD is flexible within 2024 and ideal start date would be around September. The PhD student will be joining the group of Bilge Atasoy, working on adaptive transportation and logistics. The group has members with expertise on operations research, behavioural modelling and machine learning with applications in transportation. There is a vivid interaction in the group to foster collaboration and transfer of knowledge. The project will have opportunities for collaborations with leading universities worldwide. There is also the opportunity to get teaching experience in topic-wise related courses.

Requirements
Specific Requirements

We are looking for a candidate who has operations research background and also preferably interest and knowledge in behavioral models in transportation. As the project is a multi-faceted one, we expect candidates with an appreciation of social challenges in the context of implementing new sustainable frameworks and business models.

• Master of Science (MSc) diploma in Operations Research, Transportation, Logistics, Industrial Engineering, Applied Mathematics 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).

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 .

Additional Information
Benefits

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 € 2770 per month in the first year to € 3539 in the fourth year. 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.

For international applicants, TU Delft has the Coming to Delft Service . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.

Selection process

Are you interested in this vacancy? Please apply no later than 29 March 2024 via the application button and upload:

A motivation letter (1 page) explaining your motivation and ambitions related to the PhD position and your relevant skils.

Your CV.

Your MSc Thesis and/or 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.

Notes:

• A pre-employment screening can be part of the selection procedure.
• You can apply online. We will not process applications sent by email and/or post.
• Please do not contact us for unsolicited services.

For information about the application procedure, please contact Anita van Vianen, HR Advisor: [email protected] .

Additional comments

For more information about this vacancy, please contact Bilge Atasoy, e-mail: [email protected] .

Website for additional job details

https://www.academictransfer.com/337802/

Work Location(s)

Number of offers available

1

Company/Institute

Delft University of Technology

Country

Netherlands

City

Delft

Postal Code

2628 CD

Street

Mekelweg 2

Geofield

Where to apply

Website

https://www.academictransfer.com/en/337802/phd-position-adaptive-optimization-a…

Contact

City

Delft

Website

http://www.tudelft.nl/

Street

Mekelweg 2

Postal Code

2628 CD

STATUS: EXPIRED