Research Associate / Doctoral Candidate (m/f/d) Learning Analytics for Social Learning and Connected

Research Associate / Doctoral Candidate (m/f/d) Learning Analytics for Social Learning and Connectedness

, Academic staff

The Professorship for Learning Analytics (LEAPS) at the TUM School of Social Sciences and Technology, Technical University of Munich, is seeking a Research Associate / Doctoral Candidate (m/f/d) Learning Analytics for Social Learning and Connectedness

The position is TV-L E13, 50%, initially limited to 3 years. 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.

Project Description

When students communicate online through forums, collaborative documents, or chat, they leave traces of how they interact and connect with each other. The doctoral researcher will develop computational indicators that capture these patterns from digital communication data, model how learning relationships form and evolve, and use these insights to build interventions that support connectedness among learners. The position includes some teaching in the areas of educational technology and learning analytics.

Your Profile

• Completed Master's degree (or equivalent) in social psychology, computational linguistics, computational social science, linguistics, communication science, or a related field with a strong quantitative profile
• Experience with quantitative research methods and data analysis
• Knowledge of network science methods, natural language processing, or computational text analysis is an advantage
• Proficiency in statistical or programming tools (e.g., R, Python)
• 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 language skills are an advantage
• Ability to work in an interdisciplinary team

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
• 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 (50%)

Please send your complete application (motivation letter, CV, transcripts, Master's thesis or relevant publications, contact details of references) as a PDF to:

Equal Opportunity Statement

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

When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

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