Master Thesis in Causal AI in Manufacturing

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

We as Bosch have many manufacturing plants with highly automated production lines. To enable the minimization of costs and ramp-up time, it is crucial to understand the cause-effect relationships between various process parameters and the final product functionality. Leveraging our collected manufacturing data and causal learning techniques bears a great potential for gaining insights into manufacturing processes and solving domain questions, such as quality prediction and root cause analysis.

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

Qualifications

• Education: Master studies in the field of Computer Science, Mechanical Engineering, Electrical Engineering, Physics, Mathematics, or comparable with very good grades
• Experience and Knowledge: in Python, ideally first practical experience in machine learning
• Personality and Working Practice: communicative, responsible and self-motivated
• Languages: fluent in English, German is a plus

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