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

Robert Bosch GmbH
Magstadt, Germany
about 1 month ago
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Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Job source
de.indeed.com

Tech stack

Artificial Intelligence Machine Learning Reinforcement Learning Information Technology Recurrent Neural Networks

Job 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.

Requirements

  • 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

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