- Analyze and structure technical requirements from automotive OEMs.
- Develop LLM-based solutions for requirement understanding, classification, and validation.
- Build and evaluate RAG pipelines for engineering documents and domain knowledge.
- Explore agentic AI workflows for complex engineering tasks.
- Apply model checking and formal verification to validate system requirements and designs.
- Investigate using LLMs to generate or synthesize formal models and code for model-checking tools.
- Design experiments, evaluate results, and document findings.
- Collaborate with AI researchers, software engineers, and domain experts.
- Currently studying Computer Science, Artificial Intelligence, Software Engineering, Mathematics, Electrical Engineering, or a related field.
- Basic knowledge of machine learning, natural language processing, or LLMs.
- Programming experience in Python.
- Interest in one or more of the following:
- LLMs and prompt engineering
- AI agents
- RAG and vector databases
- Formal methods and model checking
- Model synthesis / code generation
- Automotive requirements engineering
Nice to Have
- Experience with LLM frameworks such as LangChain, LlamaIndex, or similar.
- Familiarity with formal verification tools or languages such as NuSMV, SPIN, UPPAAL, TLA+, or temporal logic.
- Experience with embeddings, knowledge graphs, or document-processing pipelines.
- Familiarity with Git, Docker, and software development practices.
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