Technical University of Munich
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PhD / Doctoral Candidate in Social Interaction in AI-Supported Learning Environments Technical University of Munich in Germany
Degree Level
PhD
Field of study
Computer Science
Funding
TV-L E13, 75% position, limited to three years. The role is funded by the Dieter Schwarz Foundation and the TUM Institute for Advanced Study (TUM-IAS). Includes doctoral training through the TUM Graduate School, research visits to UC Irvine, and access to TUM research infrastructure.
Deadline
Oct 7, 2026
Country
Germany
University
Technical University of Munich

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About this position
Technical University of Munich (TUM) is advertising a Research Associate / Doctoral Candidate (PhD) position in Understanding and Improving Social Interaction in AI-Supported Learning Environments.
The role is based at the Professorship for Learning Analytics (LEAPS) at the TUM School of Social Sciences and Technology and is part of a broader collaboration with Prof. Di Xu and her team at UC Irvine. The project sits at the intersection of learning analytics, AI-supported education, computational methods, causal inference, social interaction, and higher education.
The doctoral researcher will study how productive peer relationships form and develop in technology-mediated learning environments, and how AI can support group formation, interaction, feedback, prompts, recommendations, and other educational interventions. The work includes analysis of rich longitudinal student interaction data, development and validation of measures and models of peer interaction and relationship formation, and design/evaluation of AI-assisted tools and interventions. The project also offers opportunities to work with instructors and institutional partners, including randomized controlled trials and other rigorous research designs.
Funding: TV-L E13, 75% position, limited to three years. The position is funded by the Dieter Schwarz Foundation and the TUM Institute for Advanced Study (TUM-IAS). The post includes doctoral training through the TUM Graduate School, research visits to UC Irvine, and access to TUM’s research infrastructure.
Eligibility highlights: completed Master’s degree or equivalent in a relevant quantitative field; strong quantitative and programming skills (Python/R); experience with statistical modelling, machine learning, causal inference, NLP, social network analysis, longitudinal analysis, or psychometrics is welcome; excellent English and demonstrated academic writing ability.
How to apply: submit a single PDF with motivation letter, CV, transcripts, Master’s thesis or relevant publications, and reference contact details to [email protected]. Questions can be directed to [email protected].
Deadline: 7 October 2026.
Funding details
TV-L E13, 75% position, limited to three years. The role is funded by the Dieter Schwarz Foundation and the TUM Institute for Advanced Study (TUM-IAS). Includes doctoral training through the TUM Graduate School, research visits to UC Irvine, and access to TUM research infrastructure.
What's required
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 with empirical data are required; experience with statistical modelling, computational methods, machine learning, or causal inference is particularly welcome. Programming skills such as Python or R are expected. Helpful experience includes learning analytics, natural language processing or computational text analysis, social network analysis, longitudinal or sequence analysis, psychometrics or measurement, and translating empirical findings into design decisions. Applicants should have interest in interdisciplinary research at the intersection of data analysis, AI, social interaction, and learning sciences, plus strong academic writing ability and excellent written and spoken English.
How to apply
Send a single PDF application including a motivation letter, CV, transcripts, Master’s thesis or relevant publications, and reference contact details to [email protected]. Direct questions to Prof. Poquet at [email protected]. Apply by 2026-10-07.
More information can be found here
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