学习与机器分析方向博士职位

Research Associate / Doctoral Candidate (m/f/d) Analytics for Learning with Machines (ALMA)

04.03.2026, Academic staff

The Professorship for Learning Analytics (LEAPS) at the TUM School of Social Sciences and Technology, Technical University of Munich, is seeking, within the DFG/ANR-funded project “Analytics for Learning with Machines” (ALMA)

The position is TV-L E13, 75%, limited to 3 years, funded by the Deutsche Forschungsgemeinschaft (DFG). The project is a Franco-German collaboration with the Institut de Recherche en Informatique de Toulouse (IRIT), Université de Toulouse. Applications are reviewed on a rolling basis (first come, first served). Application deadline: 25 March 2026.

About Us

The candidate will be a part of the LEAPS research group (LEarning Analytics and Practices in Systems) 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.

Your Tasks

Students increasingly learn with LLMs, but we don’t yet have the tools to tell when that collaboration is effective. This PhD develops computational approaches to characterise the quality of student-LLM collaboration, opening new territory for learning analytics. The doctoral researcher will work within the DFG/ANR project “Analytics for Learning with Machines” (ALMA) on developing analytical methods and computational models that support collaboration quality between students and LLMs in educational settings. The research combines approaches from learning analytics, computational modelling, and complex dynamical systems to develop and validate indicators of human-AI interaction processes in learning environments. The position involves close collaboration with the project team at IRIT in Toulouse, led by Professor Mar Perez-Sanagustin.

Your Profile

• Completed Master’s degree (or equivalent) in a STEM discipline (e.g., mathematics, physics, biology, computer science), data science, computational cognitive science, computational neuroscience, or a related field with a strong quantitative profile • Experience with computational methods, statistical modelling, or machine learning • Programming skills (e.g., Python, R) • Interest in interdisciplinary research at the intersection of data analysis and learning sciences • Interest in education and learning as an application domain • Ability to work independently • Demonstrated academic writing ability (e.g., Master’s thesis, publications, or conference contributions) • Excellent written and spoken English; German and/or French language skills are an advantage • Willingness to undertake research stays at IRIT, Toulouse

What We Offer

A research environment that rewards intellectual courage and hard work, gives you the freedom and support to pursue ideas that challenge the status quo, and where you will learn a great deal. • Excellent mentorship and academic supervision • Strong international and local network • Doctoral training through the TUM Graduate School • Franco-German research collaboration with IRIT, Université de Toulouse • Active involvement in academic communities (e.g., SoLAR, EATEL) • Flexible working arrangements • Access to the excellent research infrastructure of TUM and the Munich Data Science Institute • Remuneration according to TV-L E13 (75%)

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

TUM is an equal opportunity employer committed to increasing the proportion of women in its workforce. Applications from women are therefore expressly encouraged. Candidates with disabilities who are otherwise equally qualified will be given preference.

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:

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Kontakt: office.lea@sot.tum.de

More Information

http://Please apply https://tumapply.aet.cit.tum.de/job/detail/afe6b17e-106f-4fa8-9355-c81d678e1e51