Master Thesis AI Assistant for Safety Requirements Engineering
Feladatok
- During your assignment you will develop a Generative AI-powered interface that allows users to query from requirement engineering tool chain data using natural language questions related to safety requirements and trace information.
- You will implement techniques to improve the accuracy and relevance of retrieved information, considering the context and semantics of the queries; design and implement a module that automatically generates comprehensive reports summarizing safety trace analysis and requirements compliance based on requirement tools chain data. To effectively communicate key findings and potential risks, you will explore different report formats and visualization techniques.
- Furthermore, you will develop a mechanism to automatically verify the consistency and completeness of safety traces between system documentation (e.g., requirements specifications, safety manuals) and the data captured within requirement tools chain Enterprise. You will also identify and flag potential discrepancies or gaps in traceability, alerting users to areas requiring further investigation.
- You will also take an active role in evaluating the performance of the Safety REQ AI Agent in terms of accuracy, efficiency, and user satisfaction through case studies and user feedback and in validating the agent's ability to improve the effectiveness of safety trace analysis and requirements review processes.
- Last but not least, you will design experiments for the agent that allows the comparison with manual process of using requirement tools chain. Aiming to improve the process by X% using the new AI agent (where X would be your expected improvement after some initial research).
A profilod - Education: Master studies in the field of Computer Science, Software Engineering, Embedded Systems, Artificial Intelligence or comparable
- Experience and Knowledge: in Python; Anaconda; Generative AI (GenAI); Large Language Models (LLM); JSON; JavaScript; HTML; FastAPI; GitHub; Retrieval-Augmented Generation (RAG) and Transformer architectures
- Personality and Working Practice: you are a trustworthy and enthusiastic person with a meticulous way of working
- Languages: very good in English, German is a plus
- Education: Master studies in the field of Computer Science, Software Engineering, Embedded Systems, Artificial Intelligence or comparable
- Experience and Knowledge: in Python; Anaconda; Generative AI (GenAI); Large Language Models (LLM); JSON; JavaScript; HTML; FastAPI; GitHub; Retrieval-Augmented Generation (RAG) and Transformer architectures
- Personality and Working Practice: you are a trustworthy and enthusiastic person with a meticulous way of working
- Languages: very good in English, German is a plus
Kapcsolat és további információ
Start: according to prior agreement
Duration: 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.
Need further information about the job?
Dr. Asim Abdulkhaleq (Functional Department)
+49 711 811 43512
#LI-DNI
Rólunk
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