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

- **Company:** Robert Bosch GmbH
- **Location:** Magstadt, Germany
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Machine Learning, Reinforcement Learning, Information Technology, Recurrent Neural Networks
- **Published:** August 5, 2026
- **Apply:** https://de.indeed.com/viewjob?jk=e7c95ae2cef88f3d

## About the Role

* 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

## Description

* 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.


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