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PhD Position in Generative AI for Automotive Software Engineering

04.08.2026, Academic staff

Fully funded PhD position (TV-L E13) at the Technical University of Munich (TUM) in Generative AI for Automotive Software Engineering. The project focuses on LLMs, VLMs, Retrieval-Augmented Generation (RAG), agentic AI, software-defined vehicles, and AI-supported software engineering in collaboration with academic and industrial partners.

Requirements

  • 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

Tasks

  • 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

We Offer

  • Flexible working hours with home office options
  • A friendly, international, and open working atmosphere with state-of-the-art research infrastructure
  • Access to modern computing and GPU infrastructure for the development and evaluation of LLM-, VLM-, and agentic AI solutions
  • Research across the entire automotive software development lifecycle, from requirements and software architecture to code generation, testing, deployment, and maintenance
  • Participation in a major national research project involving leading academic and industrial partners
  • The opportunity to contribute your own ideas, define research priorities, and shape the future of AI-supported automotive software engineering
  • Close collaboration with automotive manufacturers, suppliers, technology companies, and research institutions
  • Comprehensive scientific supervision and support for pursuing a doctorate
  • Opportunities to publish at high-ranking international conferences and in scientific journals
  • A full-time position with payment according to TV-L E13

Application

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.

Please send all application documents as a single PDF file. Applications will be reviewed on a rolling basis until the position is filled.

Contact:
nenad.petrovic@tum.de, marie-luise.neitz@tum.de, alex.lenz@tum.de

The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.

Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: nenad.petrovic@tum.de