Thesis Machine learning and game theory for future urban mobility

  • Robert-Bosch-Campus 1, 71272 Renningen, Germany
  • Full-time
  • Legal Entity: Robert Bosch GmbH

Company Description

Do you want beneficial technologies being shaped by your ideas? Whether in the areas of mobility solutions, consumer goods, industrial technology or energy and building technology – with us, you will have the chance to improve quality of life all across the globe. Welcome to Bosch.

The Robert Bosch GmbH is looking forward to your application!

Job Description

Established means of transportation face major societal and ecological challenges. New paradigms are arising to address these challenges, such as shared mobility and congestion pricing (city tolls). These paradigms have machine learning and multi-agent aspects at their core. This presents an opportunity for research that is highly relevant and involves exciting methods and theories.

The master's thesis would be part of this research. The idea is to use a combination of machine learning and game theory on available data sets, e.g., bike sharing or traffic congestion data, to address tasks such as demand forecasting and congestion pricing:

  • Prepare data: Choose most suitable data set(s), clean and preprocess it/them.
  • Machine learning: Use basic machine learning on this data, e.g., time series analysis, recurrent neural networks (possibly do demand forecasting).
  • Game theory: Add game-theoretic aspects to address tasks such as preference elicitation, decision making for setting city tolls to optimize social welfare, etc. (potentially use economic theories).
  • Publication: Ideally, publish a paper at a top-tier journal or conference.
  • Coding and math: The thesis can be on the practical side (develop, code, train and evaluate a model), or a mix of practical and theoretical elements (also proof mathematical theorems).

Qualifications

  • Education: Master studies in the field of mathematics, computer science, economics, physics, or similar with good notes
  • Character and working practice: Good communication and team work skills
  • Experience and Knowledge: Good coding skills (ideally: Python), good math skills, familiar with machine learning and ideally background in game theory and/or economics
  • Enthusiasm: Ideally, a genuine interest in multi-agent/economic/social systems and how they can be improved using machine learning; and a will to reach concrete results
  • Languages: Very good in English

Additional Information

Start: according to prior agreement
Duration: 6 months

Requirement for this thesis is the enrollment at university. Please attach a motivation letter, your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.

Need further information about the job?
Philipp Geiger (Business Department)
+49 711 811 92277

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