We invite applications for a fully-funded PhD position in Dynamic management for multimodal transport systems. This position represents an exciting opportunity to join a newly established research team at KU Leuven, working within the renowned Centre for Industrial Management, Traffic and Infrastructure (CIB).
Our group focuses on developing innovative operational management solutions for transport and mobility challenges, benefiting from strong international collaborations. As part of CIB, you will work in an interdisciplinary international environment addressing technological developments, societal trends, and individual needs related to mobility, with a particular focus on intelligent transport systems, operational research, and transport system analysis.
Project
Urban areas worldwide face severe mobility challenges, including persistent traffic congestion, service disruptions, and fragmented multimodal transport. While emerging technologies and trends such as on-demand transport and shared mobility services offer promising opportunities, they also significantly increase the complexity of transport systems, particularly in terms of understanding and managing mobility networks effectively. This doctoral research focuses on developing novel methodologies for state estimation and control in multimodal transport networks. The research will specifically address one or more of the following topics:
Development of dynamic state estimation techniques for multimodal transport systems:
- Integration of heterogeneous data sources (loop detectors, floating car data, public transport AVL, shared mobility data)
- Real-time estimation of passenger flows and transfer patterns between modes
- Handling of missing or delayed information from different subsystems
Design of coordinated network management strategies:
- Integration of emerging mobility services with traditional public transport
- Dynamic allocation of shared infrastructure and resources among modes
- Development of transfer synchronization strategies
- Implementation of predictive control approaches for reducing intermodal transfer times
The research will investigate the combination of model-based and data-driven approaches, with a particular focus on their complementary strengths. While traditional traffic flow theory and control theory provide the foundational understanding of system dynamics, modern machine learning and artificial intelligence techniques enable the handling of complex spatiotemporal patterns and uncertainties inherent in multimodal systems. The fusion of these approaches aims to overcome current limitations in multimodal transport management, developing solutions that can scale from isolated intersections to entire urban networks while maintaining theoretical guarantees and practical applicability. The developed methods and models will be validated using both simulation platforms and real-world data from urban networks, with particular emphasis on scalability, robustness, and practical applicability in dynamic environments.
Profile
We are looking for an enthusiastic and motivated candidate with
Required Qualifications:
- Master's degree in Transportation Engineering, Electrical Engineering, Control Systems, or related fields
- Knowledge of quantitative methods used in engineering and skills in applying such methods to solve engineering problems
- Programming skills (Python, MATLAB, or similar)
- Excellent command of English (both written and spoken)
- Strong analytical and problem-solving skills
Desired Skills:
- Knowledge of control theory and state estimation
- Experience with traffic flow modeling
- Demonstrated involvement in mobility-related projects or research
Offer
We offer:
- A fully funded PhD scholarship, with an initial appointment for 12 months, to be extended for subsequent years (up to four) on the condition of a positive evaluation
- Multiple benefits including funding to attend international conferences or schools and access to state-of-the-art research facilities and computing resources
- Integration into a young, dynamic research team with strong international connections and the opportunity to collaborate with established research groups in the field
- Flexible working hours, with the possibility of working remotely for a few days a week
- A specialized doctoral training performed in the framework of the Arenberg doctoral school: https://set.kuleuven.be/phd
- An attractive salary according to KU Leuven pay scales (see https://www.kuleuven.be/personeel/jobsite/en/phd/phd-information)
- A vibrant academic environment in the historic city of Leuven
As part of our dynamic research team, you will have the opportunity to:
- Conduct high-quality research leading to publications in top journals and scientific conferences
- Participate actively in research proposal preparation and project development
- Guide master's students in their thesis projects
- Contribute to teaching activities in the master's programme where applicable
You can apply for this job via the online application tool by providing the following documents:
- Motivation letter with contact information and preferred starting date (max one A4 page)
- Description of research interests for the PhD studies (max two A4 pages)
- CV and contact information for 2 reference persons (max two A4 pages)
- Summary of the Master's Thesis (max one A4 page)
- A certificate proving your proficiency in English
Interested?
For more information please contact Prof. dr. ir. Claudio Roncoli, email: claudio.roncoli@kuleuven.be.
KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.