Employer: AUDI AG, Ingolstadt 
Academic partner: Technical University of Munich, Heilbronn Campus, Chair of Software Engineering and AI 
Duration: 3 years | Start: 01.10.2026 or 01.11.2026

This is an industry doctorate. You will be employed by AUDI AG and supervised academically at TUM, where the doctoral degree will be awarded. The position combines access to production enterprise systems with the freedom and the time to produce peer-reviewed research.

Research focus

Large enterprise systems still run on decades-old code bases whose documentation is stale, whose specifications exist only implicitly in the code, and whose failure has direct business consequences. Generative AI and agentic coding tools promise to accelerate the modernization of such systems, but current practice is largely ad hoc: transformations are neither traceable to a specification nor verifiable against the behavior of the original system, which makes them unusable under enterprise governance constraints.

This position investigates how modernization can instead be made spec-driven, traceable, and verifiable, combining deterministic program analysis with agentic AI components. The work is grounded in real legacy systems, predominantly COBOL and Java, starting from smaller modernization scenarios and progressing toward larger monolithic applications.

Research questions

Your doctoral work will address questions such as:

• How can specifications be recovered from legacy artifacts (source code, tests, configuration, runtime traces, tribal knowledge) when documentation is absent or unreliable? What form should such specifications take to be both machine-actionable and reviewable by humans?
• Which agentic orchestration strategies scale to systems that exceed any context window? How should deterministic analysis (static analysis, type information, dependency graphs, test generation) and LLM components be divided and combined?
• How can traceability be maintained across legacy artifact, recovered specification, and generated target code, such that every transformation decision can be audited after the fact?
• Which verification and governance mechanisms make agentic modernization acceptable in regulated, mission-critical environments? What is the role of differential testing, contracts, and executable specifications?
• How do we evaluate such systems scientifically? What benchmarks, baselines, ablations, and failure-mode taxonomies distinguish generalizable contributions from engineering progress?
• How is domain knowledge embedded in a legacy system preserved rather than silently discarded during modernization?

You will not be expected to answer all of these. Sharpening them into a coherent thesis is part of the work.

Your qualifications

Required

• A very good Master's degree in computer science, software engineering, AI, or a closely related field
• Strong foundations in software architecture, software evolution, testing, and debugging, and the ability to reason systematically about nontrivial systems
• Good programming skills in at least one modern language (e.g., Python, Java, C++, C#, Go, Rust, Kotlin), plus experience with version control, automated testing, and build systems
• Evidence of research potential, through the Master's thesis, research projects, publications, open-source contributions, or technically substantial software projects
• Willingness to design reproducible experiments, benchmarks, baselines, and evaluation metrics, and to engage critically with the scientific literature
• Ability to assess the limitations, failure modes, and reproducibility of AI-assisted software engineering systems critically
• Proficient written and spoken English. German is beneficial but not required.

Advantageous

• Interest in legacy modernization, reverse engineering, and architecture transformation
• Prior exposure to COBOL, mainframes, transaction processing, or enterprise applications
• Hands-on experience with LLMs and AI-assisted software development, including coding agents such as Claude Code, Codex, Cursor, GitHub Copilot, or Gemini CLI
• Experience with APIs, databases, containers, distributed systems, or cloud-native development
• Experience developing large-scale software systems

Candidates are not expected to cover every area above. We particularly welcome applicants with strong depth in one relevant field, such as program analysis, software testing, AI for software engineering, compiler construction, software architecture, or agentic systems, and the ability to expand into adjacent areas during the doctorate.

What we offer

We offer you an exciting and challenging project within a dynamic and collaborative research environment, positioned directly at the intersection of current AI research and real-world enterprise software engineering. Access to production legacy systems, enterprise experts, architects, and governance teams is ensured through our industrial cooperations, giving the work immediate relevance to practice as well as scientific novelty. Employment is with AUDI AG under its collective agreement. Academic supervision at TUM, industrial supervision at Audi, with both sides aligned on the setup.

Location

Primary place of work is Ingolstadt, with regular presence at the TUM chair in Heilbronn.

Applications

Please send a CV, a cover letter explaining your research interests and motivation, a brief summary of previous work experience, relevant certificates and transcripts, and contact details for at least two referees to recruitment@seai.cit.tum.de. Applications will be reviewed on a rolling basis until the position is filled.

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.

Location: TUM Campus Heilbronn (remote optional)
Start Date: As soon as possible / by agreement
Working Hours: 8–20 hours/week, flexible

The Chair of Software Engineering and AI (Prof. Chunyang Chen) at TUM Campus Heilbronn is seeking a highly motivated student to contribute to our research project HAIC-SE – Human-AI Collaboration in Software Engineering. The project investigates how developers and AI systems (LLMs, coding agents) can collaborate more effectively across the software development lifecycle. We develop collaboration patterns, adaptive interfaces, and evaluation methods in close cooperation with an industry partner.

Your Tasks

• Design, develop, and evaluate prototypes for human-AI collaboration in software development
• Conduct literature reviews and help synthesise the state of the art
• Support the preparation and execution of user studies and empirical evaluations
• Analyse qualitative and quantitative data from experiments and developer interactions
• Contribute to scientific publications at top-tier venues

Your Profile

• Student Status: Currently enrolled at TUM (Computer Science, Software Engineering, Information Systems, or related field)
• Programming: Strong software engineering skills; proficiency in Python and/or TypeScript; experience building non-trivial software projects
• AI Tools: Hands-on experience with current AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude Code, Codex) and/or LLM APIs (OpenAI, Anthropic, Gemini, etc.)
• HCI Methods (plus): Familiarity with user study design, qualitative analysis, or UX evaluation methods is a strong advantage
• Research Interest: Curiosity about how humans and AI systems interact in practice; willingness to engage with academic literature
• Language: Proficient in English (written and spoken); German is a plus but not required

We Offer

• Direct involvement in a funded research project at the intersection of SE, HCI, and AI
• A collaborative, international team working on one of the most relevant topics in modern software engineering
• Flexible hours compatible with your study schedule
• Remuneration according to standard TUM student assistant rates

Application

Please send your application by email to:

recruitment@seai.cit.tum.de
Subject: [HiWi Application] HAIC-SE Research - [Your Name]

Include the following:

• Short motivation (0.5–1 page): Why does this topic interest you? What relevant experience do you bring?
• Curriculum vitae
• Transcript of records
• (Optional) Links to relevant projects, GitHub profile, or prior work