PhD Position in Multimodal Demand Management and Optimization under Uncertainty

Design adaptive multimodal transport systems that anticipate disruption, integrate behavioral models, and optimize mobility in real time in a new ERC-funded project at TU Delft.

Job description

The scientific challenge

Urban transport systems generate ever-growing data streams, yet they continue to fail during disruptions. One key reason is that short-term operations and long-term planning are designed in silos. As a result, supply and demand adjustments occur out of sync, leading to congestion, inefficiencies, and service breakdowns.

The ERC Consolidator project TRANSFORM addresses this gap by developing a unified framework for resilient multimodal systems under uncertainty. The project reframes multimodal mobility as a coupled system with three interacting players, mobility service suppliers, infrastructure operators, and users whose decisions and reactions unfold on different time scales. What makes TRANSFORM distinctive is the way it fuses dynamic uncertainty modeling, behaviorally informed demand management, and iterative optimization across multiple decision layers into one coherent methodology.

Your research role

In this PhD position you will develop the scientific core that enables real-time, coordinated multimodal demand management. You will design a modelling and optimization framework that:

• Explicitly captures the dynamic feedback loop between supply and demand
• Integrates behavioral choice models with network state information
• Supports forward-looking optimization under uncertainty
• Designs individualized multimodal services that improve efficiency while maintaining service quality and user preferences

Your work will result in a novel, scalable modelling framework that advances both theory and application in resilient multimodal transport systems.

Where you will work

Your home base will be the SUM Lab in the Department of Transport & Planning (T&P) within the Faculty of Civil Engineering and Geosciences. You will work closely with domain experts Bilge Atasoy and Maarten Kroesen, and collaborate with fellow PhDs and researchers across behavioral modelling, optimization, and transport systems analysis.

The position is embedded in a prestigious ERC consolidator grant, offering strong scientific visibility and opportunities for international collaboration.

Job requirements

• A Master's degree in a relevant field, i.e. Applied mathematics, Machine Learning, or Computer science. Engineering degree with strong methodological backgrounds related to these topics is considered as well.
• Solid knowledge of machine learning, optimization, and discrete choice modelling/ behavioral models.
• Strong programming skills (e.g. Python, C++, Java).
• Ability to work both in a project team, but also independently and take leadership and responsibility for research tasks.
• Interest in interdisciplinary collaboration and contributing to teaching and lab activities.
• Excellent communication skills in English, both written and oral.

TU Delft (Delft University of Technology)

You will be part of Delft University of Technology, a top international university combining science, engineering and design, with research and education addressing challenges in areas including mobility.

Faculty of Civil Engineering and Geosciences

The Faculty of Civil Engineering & Geosciences (CEG) conducts international research and education in areas including traffic and transport, with research projects conducted in close cooperation with a wide range of research institutions. CEG supports open science and integrates it in research practice.