Master Thesis Application of Physics-Informed Machine Learning for Grid-Tied Inverter Control

Robert Bosch Group; Renningen; Vor Ort; EUR 13.000 - 17.000; Vollzeit

Zusammenfassung

The Robert Bosch Group in Germany invites applications for a Master Thesis: Application of Physics-Informed Machine Learning for Grid-Tied Inverter Control. The project combines hands-on lab work with advanced AI-driven control concepts to enhance grid-tied inverter performance. Collaboration across engineering and IT faculties is expected.

Start is negotiable; duration is 6 months. Enrollment at a university is required.

Qualifikationen

• Master studies in the field of Engineering, Mathematics, Information Technology or comparable.
• Experience applying AI methods, system modelling and control engineering.
• English language skills are required.

Aufgaben

• Develop a physics-informed current controller for a grid-tied inverter.
• Evaluate the linear regulator and the predictive controller as reference points and establish their boundaries.
• Research current AI-based implementations in the literature.
• Develop a concept to improve AI-based black-box implementations and mitigate drawbacks in grid-tied inverter applications.
• Select an AI-based concept, potentially including machine learning, recurrent neural networks or reinforcement learning; model the system if needed.

Kenntnisse

AI methods

System modelling

Control engineering

Power electronics

Ausbildung

Master studies in Engineering/ Mathematics/ Information Technology or comparable

Jobbeschreibung

Master Thesis Application of Physics-Informed Machine Learning for Grid-Tied Inverter Control

Full-time

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!

• As a part of your Master thesis, you will develop a physics-informed current controller for grid-tied inverter control.
• You will evaluate the linear regulator and the predictive controller as reference points, as well as establish their boundaries.
• In addition, you will research current AI-based implementations in the literature.
• Furthermore, you will be responsible for developing a concept to improve the implementation of AI-based black boxes and mitigate their drawbacks in grid-tied inverter applications.
• Last but not least, you will select an AI-based concept, which may include machine learning, recurrent neural networks or reinforcement learning. If necessary, you will model the system alongside the implemented controller.

• Education: Master studies in the field of Engineering, Mathematics, Information Technology or comparable
• Experience and Knowledge: in the application of AI methods, system modelling and control engineering; in power electronics advantageous
• Personality and Working Practice: you are a team player with good communication skills who is able to work independently, proactively and meticulously
• Work Routine: office and laboratory work required, partially mobile working possible
• Languages: good in English

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?
Paul Kießling (Functional Department)
[phone]
TobiasBeck (Functional Department)
[phone]

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