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Technical University of Munich

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PhD Position in Generative AI for Automotive Software Engineering Technical University of Munich in Germany

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available
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Country

Germany

University

Technical University of Munich

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Keywords

Computer Science
Electrical Engineering
Information Technology
Software Engineering
Robotics
Large Language Models

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About this position

PhD position in Generative AI for Automotive Software Engineering at the Technical University of Munich (TUM), Germany.

This fully funded doctoral opening focuses on LLMs, VLMs, Retrieval-Augmented Generation (RAG), agentic AI, software-defined vehicles, and AI-supported software engineering. The project is carried out in collaboration with academic and industrial partners and covers the full automotive software lifecycle, including requirements, architecture, code generation, testing, deployment, maintenance, verification, security analysis, and monitoring.

Research topics include: LLM- and VLM-based methods for automotive software engineering; human-in-the-loop agentic AI systems; RAG pipelines using automotive standards, technical documentation, software models, and source-code repositories; AI-supported code generation and transformation; DevOps and DevSecOps automation; automated test-case generation; vulnerability analysis; debugging; and integration of locally deployable models in privacy-sensitive industrial environments.

Eligibility highlights: a completed master's degree in Computer Science, Artificial Intelligence, Software Engineering, Robotics, or a related field; strong programming skills, preferably in Python; knowledge of machine learning, LLMs, VLMs, or generative AI; and an interest in automotive software engineering and future mobility technologies. Excellent English, strong analytical skills, independence, and the ability to work in an interdisciplinary international team are expected. Experience with RAG, AI agents, model-driven engineering, DevOps, DevSecOps, or automated testing is advantageous.

Funding: fully funded PhD position with payment according to TV-L E13, full-time, with access to modern computing and GPU infrastructure.

How to apply: send a single PDF containing your CV, relevant certificates and transcripts, and a brief statement of your experience, research interests, and motivation for pursuing a doctorate. Applications are reviewed on a rolling basis until the position is filled.

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

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

More information can be found here

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