Master Thesis Causal AI in Manufacturing
Master Thesis Causal AI in Manufacturing
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Job Description
At Bosch we have many manufacturing plants with highly automated production lines. In order to minimize costs and rampup time it is crucial to understand the causeeffect 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 coauthor 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 selfmotivated 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)
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
