Master Theis Application of Physics-Informed Machine Learning for Grid-Tied Inverter Control
The role involves developing a physics‑informed current controller for grid‑tied inverters, evaluating linear and predictive regulators, and researching AI‑based approaches. The candidate will design and implement an AI concept (e.g., RNN or RL) while addressing black‑box limitations in power‑electronics applications.
Skills required
About this role
- 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.
Qualifications
- 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
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?
Paul Kießling (Functional Department)
+49 152 04696974
Tobias Beck (Functional Department)
+49 152 58813496
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