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Master Thesis Machine Learning Based Rider Analysis for eBikes

Master Thesis Machine Learning Based Rider Analysis for eBikes

Robert Bosch GmbHRenningen, DE
Vor 30+ Tagen
Stellenbeschreibung

Master Thesis Machine Learning Based Rider Analysis for eBikes

Robert-Bosch-Campus 1, 71272 Renningen, Germany

Full-time

Robert Bosch GmbH

Company Description

At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid : we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference.

The Robert Bosch GmbH is looking forward to your application!

Job Description

  • In your thesis, you will implement sequence models in the field of machine learning, in the context of eBike rider analysis for increasing the pedaling efficiency.
  • You will experiment with different training strategies to evaluate and compare the performance of the models.
  • Additionally, you will assess the influence of various sensor values as input data for the sequence models to understand their effect on model performance.

Qualifications

  • Education : Master studies in Computer Science, Artificial Intelligence, Mechatronics, Electrical Engineering, Cybernetics or comparable
  • Experience and Knowledge : in machine learning, good Python programming skills, practical experience with ML libraries like PyTorch or TensorFlow and neural network training, ideally first experiences with sequence models (LSTMs, GRUs, transformers, ...), knowledge in the fields of state estimation and vehicle dynamics are an advantage
  • Personality and Working Practice : you are a motivated person who likes to try new things and learn, with an independent and systematic approach to tasks
  • Languages : fluent in English or German

    • Additional Information

    Start : according to prior agreement

    Duration : 6 months

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

    Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.

    Need further information about the job?

    Sophia Steinhof (Functional Department)

    [email protected]

    LI-DNI