Thesis in Machine Learning Based Virtual Sensor for eBikes

• During your master thesis, you will apply and adapt state-of-the-art sequence models from recent publications in the field of machine learning in the context of virtual sensors for the smart and safe e-bike of the future.
• You will train and compare models to assess their suitability and performance while experimenting with the training scheme.
• Ideally, a demonstrator of the final approach will be realized to validate the functionality.

Qualifications:

• Education: Master studies in the field of Computer Science, Artificial Intelligence, Mechatronics, Electrical Engineering, Cybernetics, or comparable with good grades.
• Experience and Knowledge: In the field of 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, etc.); knowledge in the fields of state estimation and vehicle dynamics are an advantage.
• Personality and Working Practice: Motivated to try out and learn new things with an independent and systematic approach to the task.
• 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.