Postdoc Adaptive Optimization and Learning Methods for Transportation Systems
Delft University of Technology (TU Delft)
Would you be interested in representing the adaptive nature of decision making within agent-based models for transportation?
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, with a continuous input from the operational level on the delays in different parts of the network, the fleet allocation can be adapted at the tactical level. Similarly, depending on the trends in behavior for a given delivery service, the network design can be adapted.
This postdoctoral position focuses on the adaptive optimation models within ADAP-OR that will exploit transport optimization models and machine learning hand in hand to reach the self-learning capability. An agent-based framework will be developed where the agents and their interactions in the transport system are represented. Model-based learning will be the core of the self-learning capability where transportation domain knowledge is combined with the data-driven techniques in order to have tractable and effective methodologies. This is challenging as transportation problems are at the network level and involve different entities with different characteristics who are making decisions related to different time scales. As the postdoctoral researcher, you 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 position is available as of 01 Jan 2024 with flexibility in terms of the start date. You 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. You will also have the opportunity to get teaching experience in topic-wise related courses.
We are looking for a candidate who has operations research background, is interested in integrating machine learning techniques and optimization and also preferably has transportation research knowledge. 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, preferably in combination with economics, behavioral modeling and/or policy analysis.
- PhD in Operations Research, Transportation, 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
- Good communication skills
Conditions of employment
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities. The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.
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TU Delft (Delft University of Technology)
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For more information about this vacancy, please contact Bilge Atasoy (firstname.lastname@example.org)
Are you interested in this vacancy? Please apply no later than 21 November 2023 via the application button and upload:
- A motivation letter (1 page) explaining your motivation and ambitions related to the postdoctoral position and your relevant skills.
- Your CV.
- Your PhD Thesis and/or publications (if applicable).
- 4. Name and contact details of at least two referees (preferably including your PhD thesis supervisor).
- 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.
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