Your tasks

• Researching and evaluating modern AI approaches such as agentic workflows and domain adaptation based on existing large language models (LLMs)
• Designing and implementing a knowledge base using Retrieval-Augmented Generation (e.g., vector search, hybrid retrieval, contextual query rewriting), including evaluation of answer quality and traceability
• Developing interfaces to the motion library and integrating the trajectory planning software to simulate and validate robot programs via function calling within a sandbox environment
• Preparing and generating synthetic as well as real-world example data using existing documentation, tests, and training materials
• Evaluating different local and cloud-based LLMs with regard to performance, cost, data privacy, and reasoning capabilities, as well as testing and documenting overall system quality

Your profile

• Enrolled student in Computer Science, Mathematics, Electrical Engineering, or a comparable field
• Strong proficiency in Python and confident use of development environments such as Conda and Visual Studio Code
• Initial hands-on experience with large language models, LLM agents, or MCP/skills-based architectures
• Ideally, a basic understanding of robotics, simulation, and mathematical modeling of robotic systems
• Strong analytical thinking skills, a structured working style, and enthusiasm for applying emerging technologies in practice