Thesis in Development of a Learning Based Compositional Electrical Drive Mo at Bosch Group

Renningen, Baden-Württemberg, Germany -

Full Time

Start Date

Immediate

Expiry Date

08 Feb, 26

Salary

0.0

Posted On

10 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Python, Modelling, Dynamical Systems, Electric Drives, Simulation, Control, Data-Based Models, Differentiable Models, Optimization, Literature Research, Physical Models, Inverters, Electric Machines, Automatic Differentiation, ODE Solvers

Industry

Software Development

Description

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 The identification of accurate simulation models of electric drive systems, comprising the inverter, an electric driver, and further components, is a crucial step for the design of high-performing controllers, fault diagnosis, and many other tasks. Goal of the thesis is to develop a compositional model for electric drives that allows for simulation, identification and control using automatic differentiation techniques. The main idea is to implement differentiable models for components of an electric drive that can be freely combined to an overall system model. You will familiarize yourself with physical models of electric drives (electric machines, inverters, …). You will do literature research on existing (ML-based) approaches for the identification of electric drives. Furthermore, you will develop the dynamical physical electrical drive model combined with data-based models. Last but not least, you will implement the proof of concept to demonstrate the gradient-based optimization of the overall model for a given example system under using dynamical data with ODE solvers. Qualifications Education: studies in the field of Electrical Engineering, Cybernetics, Physics, Computer Science or comparable Experience and Knowledge: in Machine Learning and Python; modelling of dynamical Systems Personality and Working Practice: you are flexible, enthusiastic and responsible Languages: good in German and 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? David Gänzle (Functional Department) +49 711 811 49410 #LI-DNI

Responsibilities

The thesis involves developing a compositional model for electric drives for simulation, identification, and control. You will conduct literature research and implement a proof of concept for gradient-based optimization using dynamical data.