Master Thesis Machine Learning Based Rider Analysis for eBikes
Master Thesis Machine Learning Based Rider Analysis for eBikes
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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)
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
