Research Associate / Doctoral Candidate (m/f/d)

Your Profile

• Completed Master’s degree (or equivalent) in a relevant field such as data science, computer science, statistics, quantitative social science, educational technology, learning sciences, psychology, economics, or a related discipline with a strong quantitative profile
• Strong quantitative skills and experience working with empirical data; experience with statistical modelling, computational methods, machine learning, or causal inference is particularly welcome
• Programming skills (e.g., Python, R) - Experience in one or more of the following areas would be particularly valuable: learning analytics, natural language processing or computational text analysis, social network analysis, longitudinal or sequence analysis, psychometrics or measurement, and experience translating empirical findings into design decisions, such as in intervention or tool development.
• Interest in interdisciplinary research at the intersection of data analysis, AI, social interaction, and learning sciences – Interest in designing and evaluating educational tools or interventions in higher education
• An entrepreneurial and intellectually curious mindset, with ability to work independently, engage critically with empirical results, and develop research ideas
• Demonstrated academic writing ability (e.g., Master’s thesis, publications, or conference contributions)
• Excellent written and spoken English

What We Offer

• A collaborative research environment in which you can develop an independent doctoral agenda while working across complementary expertise in learning analytics, computational social science, AI-supported education, causal inference, and field experimentation.
• Close mentorship and academic supervision from Prof. Dr. Oleksandra Poquet at TUM and Prof. Di Xu at UC Irvine, with regular collaboration across both research teams. The position is based at TUM
• Access to a strong international and interdisciplinary research network at TUM and UC Irvine
• Doctoral training through the TUM Graduate School
• Research visits to UC Irvine
• Opportunities to work with rich data on student interaction and learning and to design and evaluate AI-assisted educational interventions, including randomized controlled trials
• Opportunities to study how promising interventions can be implemented and evaluated across courses and at larger scale
• Flexible working arrangements
• Access to the excellent research infrastructure of TUM, the Munich Data Science Institute, and the TUM EdTech Centre
• Remuneration according to TV-L E13 (75%)

Application

Please send your complete application (motivation letter describing your research interests and fit with the project, CV, transcripts, Master’s thesis or relevant publications, contact details of references) as a single PDF to:office.lea@sot.tum.de

Questions can be directed to Prof Poquet at sasha.poquet@tum.de Application deadline: October 7th, 2026

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