- Accurate models are critical for designing and calibrating control functions across a wide range of Bosch products. While physical equations provide a foundation modeling complex systems requires a more sophisticated approach. This PhD position offers the opportunity to develop and implement innovative hybrid model structures combining physical knowledge with datadriven techniques to achieve exceptional accuracy with minimal calibration effort.
- The initial focus will be on a joint electric drive and mechanical drivetrain system a core technology in Bosch products like EBikes automotive components and power tools. By mastering efficient hybrid model identification and control techniques you will directly contribute to Boschs ability to deliver consistently highperforming and accurate products ensuring superior performance across all operating conditions and production samples.
- This exciting project offers the chance to collaborate with machine learning experts at our advanced research headquarters in Renningen gaining valuable experience and contributing to a strategic area of Bosch innovation.
Qualifications :
- Education: excellent Masters degree or equivalent in Electrical Engineering Mechanical Engineering Cybernetics or Computer Science
- Experience and Knowledge: sound knowledge of advanced control theory machine learning and databased modeling; knowledge of tools for control and modeling (e.g. Matlab/Simulink Python)
- Personality and Working Practice: likes to develop solutions in interdisciplinary diverse teams and implement them up to the functional model; willing to take initiative and responsibility with a methodically & structured solutionoriented way of working
- Enthusiasm: enthusiastic about technical challenges
- Languages: very good knowledge of English knowledge of German is an advantage
Additional Information :
final PhD topic is subject to your university.
Start: according to prior agreement
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 support during your application
Sarah Schneck (Human Resources)
49(9352)188527
Need further information about the job
Adrian Trachte (Functional Department)
49(152)
Remote Work :
No
Employment Type :
Fulltime