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# Generative AI for Automotive Software Engineering

- **Company:** TU München
- **Location:** Ilmenau, Germany
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Automation of Tests, Code Generation, Computer Programming, Continuous Integration, Software Debugging, DevOps, Python (Programming Language), Software Architecture, Software Engineering, Verification and Validation (Software), Large Language Models, Generative AI, Hardware Infrastructure, Automotive Software, Devsecops, Vulnerability Analysis
- **Published:** August 5, 2026
- **Apply:** https://portal.mytum.de/jobs/wissenschaftler/NewsArticle_20260804_150233

## About the Role

* A completed master's degree in Computer Science, Artificial Intelligence, Software Engineering, Robotics, or a related field
 * Strong experience in software development and excellent programming skills, preferably in Python
 * Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI
 * Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps, DevSecOps, or automated testing is advantageous
 * Interest in automotive software engineering, software-defined vehicles, and future mobility technologies
 * Strong analytical and scientific skills and motivation to pursue a doctorate
 * Ability to work independently and contribute effectively to an interdisciplinary and international research team
 * Excellent written and spoken English, We look forward to receiving your application. It should include a curriculum vitae, relevant certificates and transcripts, and a brief statement describing your previous experience, research interests, and motivation for pursuing a doctorate in generative AI and automotive software engineering.

## Description

* Research and development of LLM- and VLM-based methods for automotive software engineering
 * Development of agentic AI systems with Human-in-the-Loop mechanisms for software development, testing, deployment, and maintenance
 * Design and implementation of Retrieval-Augmented Generation (RAG) pipelines using automotive standards, technical documentation, software models, and source-code repositories
 * Development of AI-supported methods for code generation, code transformation, software updates, and platform-specific software adaptation
 * Automation of automotive DevOps and DevSecOps workflows, including continuous integration, testing, verification, security analysis, deployment, and monitoring
 * Development of generative AI methods for automated test-case generation, software validation, vulnerability analysis, and debugging
 * Integration of locally deployable LLMs and VLMs into privacy-sensitive industrial development environments
 * Evaluation of the developed approaches using automotive use cases, simulation environments, and software-defined vehicle platforms
 * Publication of research results at leading international conferences and in scientific journals
 * Collaboration with academic and industrial partners within a large interdisciplinary research project


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