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

Understanding and Improving Social Interaction in AI-Supported Learning Environments 16.09.2026, Wissenschaftliches Personal The Professorship for Learning Analytics (LEAPS) at the TUM School of Social Sciences and Technology, Technical University of Munich, is seeking, within a TUM Institute for Advanced Study (TUM-IAS) funded research collaboration, a Research Associate / Doctoral Candidate (m/f/d) Understanding and Improving Social Interaction in AI-Supported Learning Environments About Us The candidate will be a part of the Professorship for Learning Analytics (LEAPS) led by Prof. Dr. Oleksandra Poquet. LEAPS investigates how data from learning environments can support agency and social networks in higher education and workplace training. The group is part of the TUM School of Social Sciences and Technology, the Munich Data Science Institute, and the TUM EdTech Centre. The position is embedded in a broader collaboration with Prof. Di Xu and her interdisciplinary research team at UC Irvine, bringing together expertise in higher education, learning sciences, learning analytics, causal inference, AI-supported education, and the design and evaluation of educational interventions. The position is TV-L E13, 75 %, limited to three years, and funded by the Dieter Schwarz Foundation and the TUM Institute for Advanced Study (TUM-IAS). Your TasksSocial interaction is central to learning, yet we still know relatively little about how productive peer relationships form and develop in technology-mediated learning environments, or how AI can be used to support these processes. This PhD will combine learning analytics, computational methods, and causal inference to understand social interaction at scale and to design and evaluate AI-assisted educational tools and interventions that support productive peer interaction, relationship formation, and learning. The position includes some teaching obligations aligned with the candidate’s expertise and offered at TUM by the Professorship of learning Analytics. The doctoral researcher will work with rich longitudinal data on student interaction and learning; develop and validate measures and models of peer interaction and relationship formation; and use these insights to design AI-assisted tools for group formation, interaction support, adaptive prompts, feedback, or recommendations. The researcher will have substantial scope to shape specific questions within this broader agenda. Prior experience with educational data or other large-scale longitudinal behavioral data would be beneficial but is not required. The project aims to connect analytics and interventions across the full research cycle. The doctoral researcher will have the opportunity to work with instructors and institutional partners to implement and test interventions in higher education courses, including through randomized controlled trials and other rigorous research designs. Promising interventions may subsequently be tested across additional courses and student populations, with opportunities to study implementation and effectiveness at larger, potentially institutional, scale. The position involves close collaboration with researchers across the TUM and UC Irvine teams. Your ProfileCompleted 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 profileStrong quantitative skills and experience working with empirical data; experience with statistical modelling, computational methods, machine learning, or causal inference is particularly welcomeProgramming 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 educationAn entrepreneurial and intellectually curious mindset, with ability to work independently, engage critically with empirical results, and develop research ideasDemonstrated academic writing ability (e.g., Master’s thesis, publications, or conference contributions)Excellent written and spoken English What We OfferA 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 TUMAccess to a strong international and interdisciplinary research network at TUM and UC IrvineDoctoral training through the TUM Graduate SchoolResearch visits to UC IrvineOpportunities to work with rich data on student interaction and learning and to design and evaluate AI-assisted educational interventions, including randomized controlled trialsOpportunities to study how promising interventions can be implemented and evaluated across courses and at larger scaleFlexible working arrangementsAccess to the excellent research infrastructure of TUM, the Munich Data Science Institute, and the TUM EdTech CentreRemuneration 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: [email protected] can be directed to Prof Poquet at [email protected] Application deadline: October 7th, 2026 Die Stelle ist für die Besetzung mit schwerbehinderten Menschen geeignet. 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