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

At Bosch, we have many manufacturing plants with highly automated production lines. In order to minimize costs and ramp-up time, it is crucial to understand the cause-effect relationships between various process parameters and the functionality of the final product. The use of collected manufacturing data and expert knowledge in combination with causal learning techniques holds great potential for gaining insight into manufacturing processes and solving domain tasks.

• In this thesis, you will develop causal learning methods based on domain knowledge and data.
• Furthermore, you will evaluate the quality of learned causal models on real manufacturing data.
• Last but not least, you will have the opportunity not only to interact with manufacturing process experts and ML scientists, but also to co-author a conference or journal publication.

Qualifications

• Education: Master studies in the field of Physics, Mathematics, Computer Science, Mechanical Engineering, Electrical Engineering or comparable with very good grades
• Experience and Knowledge: practical experience in Machine Learning; programming in Python; knowledge of causality is an advantage
• Personality and Working Practice: a self-motivated, reliable and independent person
• Languages: fluent in English

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?
Jingyi Yu (Functional Department)
+49 152 34622715

#LI-DNI

Summary

• Type: Full-time
• Function: Research