- As part of your assignment, you will investigate and compare different AI-based approaches to detecting anomalies in time series data from endurance test benches for electrical machines.
- You will select and implement a suitable model.
- Furthermore, you will optimize and adapt the model to detect the anomalies on the real test bench.
- Finally, you will evaluate the model based on test data or, optionally, in operation.
- Education: studies in Computer Science, Data Science, Artificial Intelligence, (applied) Mathematics, Physics, Automation Technology, Cybernetics, Automotive Engineering, Electrical Engineering or comparable
- Experience and know-how: in the fields of data-based modeling and machine learning (ML), good programming skills preferably in Python, experience with common AI libraries such as PyTorch or Tensorflow, basic knowledge in the field of electrical engineering and electrical drive technology is an advantage
- Personality and Working Practice: independent, systematic and reliable
- Languages: very good in English
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Gesucht: Master Thesis in AI-based Anomaly Detection for E-machine Test Benches - Renningen, Deutschland - Robert Bosch GmbH
Beschreibung
Company DescriptionAt 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
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?
Matthias Kraenzler (Functional Department)
Katharina Ensinger (Functional Department)
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