20.04.2026, Studentische Hilfskräfte, Praktikantenstellen, Studienarbeiten

Project context

In collaboration with the TUM Chair for Human-Centered Computing and TUM Think Tank, the FAIR-NET project investigates how transport networks shape accessibility, inequality and resilience across European metropolitan regions, combining transport modeling, network science and AI.

Tasks
The student assistant will support the project in:
- Collecting and processing transport and socio-demographic data (eg, OpenStreetMap, GTFS, public datasets)
- Developing automated workflows to extract, clean and process transport network data across multiple European regions
- Implementing data analysis workflows in Python
- Supporting graph-based modeling and machine learning experiments

Requirements
- Master's student in Transportation Systems, Data Science, Informatics, Computer Science, or a related field
- Strong analytical and problem-solving skills
- Good programming skills in Python
- Experience in extracting and processing network data from OpenStreetMap or similar sources (eg, using Python tools such as OSMnx)
- Experience with machine learning is desirable; knowledge of graph neural networks is a plus
- Ability to work independently and meet deadlines

What we offer
- Work on a cutting-edge interdisciplinary research project with real societal impact
- Exposure to advanced methods in network analysis and AI
- Opportunity to contribute to scientific publications, depending on performance

Conditions of employment
- Contract duration up to 10 months
- Working time of up to 18 hours/week

Application
Please send the following documents to santhanakrishnan.narayanan@tum.de:
- CV
- Academic transcripts
- Any relevant project work (eg, GitHub repository, code samples or coursework projects)

Please include the position ID [TSE_135] in the email subject. Review of applications will begin immediately and continue until the position is filled.

Kontakt: santhanakrishnan.narayanan@tum.de