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The Robert Bosch GmbH is looking forward to your application!
In recent years, neural networks / machine learning methods have attracted increasing attention due to their ability to model complex, non-linear interactions between given input and output variables. On the one hand, this property is a great strength. On the other hand, the training requires rather large data sets and the interpretability due to the black-box character is an obstacle for many engineering problems, where small data sets and physical relationships or boundary conditions play an essential role. In engineering applications, interpretable analytical equations are preferred not only for trustworthiness but also for understanding interpolations and extrapolations given the limited and heterogeneous data. Symbolic regression method is a data-driven approach for learning analytical equations. Implementations are currently limited and research in this field is ongoing.
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?
Steve Wolff-Vorbeck (Functional Department)
+49 711 811 15707
Christian Frie (Functional Department)
+49 711 811 43401
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Bosch is a global supplier of technology and services. Bosch specializes in consumer goods, industrial technology, and energy technology. It offers innovative solutions for smart homes, smart cities, connected mobility, and connected manufacturing...
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