Master Thesis in AI-based Anomaly Detection for E-machine Test Benches

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 GmbHis looking forward to your application!

Job Description

• During your thesis you will investigate and compare different AI-based approaches for the detection of anomalies in time series data on endurance test benches for e-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 it based on test data or optionally in operation.

Qualifications

• Education: Master studies in Computer Science, Data Science, Artificial Intelligence, (Applied) Mathematics, Physics, Automation Technology, Cybernetics, Automotive Engineering, Electrical Engineering or comparable
• Experience and Knowledge: in the fields of data-based modeling and machine learning (ML); with common AI libraries such as PyTorch or Tensorflow; good programming skills (preferably in Python); basic knowledge in electrical engineering and electrical drive technology is an advantage
• Personality and Working Practice: youwork independently, follow systematic approaches to tasks, and consistently deliver reliable results
• Languages: good 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?
Daniel Neyer (Functional Department)
+49 711 811 49287
Katharina Ensinger (Functional Department)
+49 711 811 92303

#LI-DNI

Summary

• Type: Full-time
• Function: Research