PhD Student AI FTE & AI-supported Productivity Measurement Software Engineering (d/m/f/x)

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Number: V000074365 What tasks do you expect? As a doctoral student (d/m/w/x), you are researching and working for over 3 years on the scientific discovery, further development and validation of the AI FTE (AI Full-Time Equivalent). The aim is to make AI-based efficiency and productivity contributions transparent, measurable and practically usable in software engineering. AI FTE serves as a calculation and transparency concept for the presentation of productivity contributions by AI-based tools, completely humanly responsible – not as a headcount revenue and not as a performance or success guarantee.

How do you fit into our team? Ready to start? If you convince us with proven excellent German and English skills and your complete application documents (letter, resume, note overview, enrollment certificate, etc.) Residence permit/work permit), you are only one click away from your chance. Small tip: In the flood of top applications we get, a complete application is your only ticket to the game. Without the complete package, you can't go on – no exceptions! You bring a very good master's degree in (economic) informatics, data science, software engineering, statistics, mathematics, industrial engineering or a comparable degree program. You bring a strong interest in empirical research, metric models and a methodically clean validation. You have a solid understanding of software engineering / SDLC and show great interest in AI-based productivity (e.g. copilots, agentic workflows, modern developer toolchains). In data analysis and modeling (e.g. Python, R, Statistics, Visualization, reproducible evaluations), you have very good skills. Your high level of communication and the ability to understand complex issues between engineering, management, finance and other stakeholders are the key to you. Ideally, you will experience productivity, efficiency or quality metrics in the software or technology environment. Knowledge of governance, process or framework design as well as first points of contact with offer, delivery or business partner models are also advantageous. If you have already gained experience in knowledge transfer, enablement formats, workshops or community work, this is a big plus.

Hybrid work possible Employee handy possible Health measures Employee discounts possible Staff Events Food supplements Lifelong learning Mobility offers Employee participation possible Coaching Employment Flexible working time possible Parking space Kantine, Café Good connection Accessibility