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Technical University of Munich

[TSE 137] Open working student (HiWi) position in the AgiMo project (C7)

At the Chair of Transportation Systems Engineering (TSE) of the Technical University of Munich (TUM), we are looking for a motivated and talented student assistant to join the research project AgiMo.

Project context

The Collaborative Research Centre (CRC) 'Data-driven agile planning for responsible mobility' (AgiMo) is a collaborative work between the Technical University of Munich and three other institutions in the country. Among others, the CRC pursues goals such as developing a new set of consistent scientific methods for mobility planning and management, and integrating new modular metrics for responsible mobility.

Tasks

The student assistant will support the project in:

  • Developing optimization models to calibrate demand and supply parameters in MATSim.
  • Building automated workflows to extract, clean, and process transport-related data.
  • Implementing data analysis workflows in Python.
  • Developing surrogate models using mathematical modeling or machine learning approaches.
  • Supporting experiments related to optimization, calibration, and hyperparameter tuning.
  • Documenting code, workflows, and results in a clear and reproducible way.

Requirements

  • Enrolled in a Master’s program in Transportation Systems, Data Science, Informatics, Computer Science, Mathematics, Engineering, or a related field
  • Strong analytical and mathematical skills.
  • Good programming skills, especially in Python.
  • Basic knowledge of Java, preferably, but not mandatory.
  • Interest or experience in optimization algorithms and hyperparameter tuning.
  • Interest or experience in machine learning.
  • Ability to work independently, communicate clearly, and meet deadlines
  • Motivation to work on transport simulation, data-driven modeling, and large-scale computational workflows.

Desirable Skills

Experience with one or more of the following would be beneficial:

  • MATSim or other transport simulation tools.
  • Data processing libraries such as pandas, NumPy, GeoPandas, or similar.
  • Optimization frameworks or libraries.
  • Machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
  • Version control using Git.
  • Working with large datasets or high-performance computing environments.

Conditions of employment

  • Contract duration up to 6 months
  • Working hours of 15 hours/week

Application

Please send the following documents to barun.das@tum.de and mohamed.abouelela@tum.de:

  • CV
  • Academic transcripts
  • Any relevant project work (eg, GitHub repository, code samples or coursework projects)

Please include the position ID [TSE_137] in the email subject. Review of applications will begin immediately and continue until the position is filled.

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