Thesis in High Performance Model Predictive Control Using Machine Learning Full-time

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 GmbH is looking forward to your application!

Job Description

Model predictive control is a promising advanced control method with the potential to improve the efficiency of electric drives. However, due to the huge online computational burden, its application is often limited to the use of simplified models and short horizons which sacrifices performance.

• During your assignment, we want to investigate how machine learning concepts such as reinforcement learning or deep neural networks can be exploited.
• Last but not least, the developed concepts are to be benchmarked with existing control concepts and can optionally be tested on the test bench.

Qualifications

• Education: studies in the field of Cybernetics, Engineering, Mathematics, Computer Science or comparable
• Experience and Knowledge: in Python DL frameworks like Pytorch, Tensorflow, or Jax; profound knowledge of machine learning and control theory
• Personality and Working Practice: you work in a autonomous as well as systematic manner and are an analytical thinker
• Languages: good 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?
Felix Berkel (Functional Department)
+49 711 811 92301

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