PhD Position in Transportation Analytics

Your profile Master's degree in Statistics, Industrial Engineering, Operations Research, Civil Engineering, Computer Science, Data Science or a related field, from a university/department with a strong international research reputation Strong mathematical and analytical skills for model formulation and optimization Demonstrated research potential, ideally with a track record of publications in relevant venues (journals such as IEEE T-ITS, INFORMS Transportation Science, Transportation Research Part B, or conferences such as SIGKDD, ITSC, CIKM) Strong programming skills in Python Strong interest in scientific research with the goal of obtaining a doctoral degree High motivation and enthusiasm for working in an interdisciplinary research environment Research focus: The successful candidate will conduct research on data-driven modeling of transportation systems, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic infrastructure, and emerging mobility solutions (e.g., electric vehicles, e-scooters, drones). We offer Self-determined work related to interdisciplinary research projects A diverse and inclusive working environment The opportunity to work in a vibrant scientific environment The possibility to partially work from home as well as a modern, well-equipped workplace on the Bildungscampus Heilbronn Access to advanced training opportunities for professional development About our group: Prof. Dr. Ziyue Li holds the Professorship in Transportation Analytics in the Department of Operations & Technology and Heilbronn Data Science Center at the Technical University of Munich, Germany. His research focuses on spatiotemporal data mining, with applications in smart transportation and smart cities, emphasizing generalizability, reliability, and robustness. Prof. Li has authored over 40 papers in top-tier AI and machine learning conferences and journals. Ranked among the Top 50 researchers in «Spatiotemporal Data Mining» and «Smart Mobility» (Google Scholar), he has received multiple prestigious international awards, including the IEEE CASE Best Conference Paper Award, INFORMS QSR Best Student Paper Award, and INFORMS DM Best Applied and Best Theoretical Paper Awards. Beyond academia, Dr. Li has extensive industrial experience and collaboration with Microsoft, The Bell Labs, Hong Kong Mass Transit Railway Co., and other leading corporates. Transportation data are inherently spatial, temporal, multi-modal, and high-dimensional. Our work addresses the challenges of Perception, Decision, and Explanation (PDE) in complex transport systems: Perception: Developing robust models to handle noisy, complex spatiotemporal data, integrating physical constraints, and generalizing from limited labels. Decision: Designing adaptive, automated decision-making tools (e.g., traffic signal control, human-vehicle coordination, logistics optimization, route planning) using reinforcement learning in dynamic environments. Explanation: Building interpretable causal models to explain patterns (e.g., congestion dynamics), enabling transparency in high-stakes decision-making. We combine statistical data mining, deep learning, and domain knowledge to design models that adapt to the physical realities of transportation systems, with the ability to generalize to new tasks and datasets. For more information about the research group, please refer to: https://bonaldli.github.io/ Application process We look forward to receiving your application documents (one-page letter outlining your motivation and research plan, transcripts of records, CV, IELTS/ TOEFL/GRE certificates if available, other certificates) by September 30, 2025, as a single PDF document via e-mail to jobs.udsm@mgt.tum.de (contact person: Ms. Elke Kröber) using the subject «PhD Transportation Analytics». Or by post to: TUM School of Management UDSM Elke Kröber Bildungscampus 2 74076 Heilbronn If you apply by post, please send us copies only, as we will unfortunately not be able to return your application documents once the process has been completed. The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. TUM strives to raise the proportion of women in its workforce and explicitly encourages applications from qualified women. Payment will be based on the Collective Agreement for the Civil Service of the Länder (TV-L) up to a classification in pay group 13 with appropriate qualifications. Note on data protection: As part of your application for a position at the Technical University of Munich (TUM), you submit personal data. Please note our data protection information in accordance with Art. 13 of the General Data Protection Regulation (GDPR) on the collection and processing of personal data in the context of your application. By submitting your application, you confirm that you have taken note of TUM's data protection information.Wirtschaftsingenieurwesen Wirtschaftsingenieurwesen Bauingenieurwesen Informatik Verkehr, Transport, Logistik Doktorand, Doktorandin Lehre & Forschung, Wissenschaft Universität Teilzeit