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Master Thesis in Causal AI in Manufacturing
Aktualität: 19.06.2024


19.06.2024, Bosch-Gruppe
Master Thesis in Causal AI in Manufacturing
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. Contact & Additional information Jingyi Yu (Functional Department) Social counselling and intermediary service for care services Discounts for employees
Master Thesis in Causal AI in Manufacturing Job ID REF231013X LocationRenningen , Germany Fields of workResearch Join asNot Applicable Job typeFull-time Start DateAccording to arrangement Apply now Welcome to Bosch 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 This location offers