Research

The configuration of electric drive systems within mechatronic systems presents a challenging engineering task due to the wide variety of options, complex physical relationships, and heavy reliance on domain-specific and experiential knowledge. In industrial practice, this knowledge is often only implicitly available, difficult to access, and only limitedly scalable.

In my research, I address these challenges by developing a knowledge-based agent-driven assistance system that integrates natural language, formalized knowledge models (e.g., ontologies), and multi-physics-based simulations. The goal is to make engineering processes more transparent, traceable, consistent, and efficient, as well as to systematically structure and make expert knowledge available.

A particular focus is on the integration of modern AI methods, domain-specific knowledge, and model-based validation to enable well-founded and verifiable decisions in early phases throughout the entire system design process.

Interested in a thesis?
I offer exciting research topics related to the development of agent-based assistance systems for the knowledge-based configuration of electric drive systems. The focus is particularly on novel software architectures that combine structured knowledge bases with modern AI methods.
Are you interested, but don’t see a suitable opening listed? Then feel free to send me an unsolicited application!