Master Thesis LLM-based Co-pilot for the ECU Test Development

Developing production tests for Electronic Control Units (ECUs) is a complex and time-consuming task. It requires domain engineers to manually extract, verify and consolidate information from a wide range of heterogeneous sources, including technical documents, spreadsheets and specialized databases. This process is not only tedious but also prone to errors due to the unstructured and fragmented nature of the data.

• The goal of your Master thesis project is to design and implement an intelligent assistant system that will support domain engineers throughout the ECU test development workflow.
• The envisioned system will integrate large language models (LLMs)-based techniques with existing engineering and software development tools to create context-aware co-pilot that can assist in retrieving relevant parameters, interpreting technical content and streamlining test creation.
• By leveraging the capabilities of LLMs, the system aims to enhance productivity, reduce manual effort, and improve the overall quality of the test development process.

• Education: Master studies in the field of Computer Science, Mathematics, Statistics or comparable with very good grades
• Experience and Knowledge: strong programming skills in Python; experiences with LLMs, MCP, RAG and/or Agentic Frameworks; experience with User Interface, Backend and FastAPI is an advantage
• Personality and Working Practice: you excel at working independently and driving your own projects forward with strong intrinsic motivation
• Languages: fluent in English