LMU Munich
Munich, Germany

PhD Researcher in Computational Text Analysis and Democratic Belief Systems – Ludwig-Maximilians-Universität München – Germany

Job Type
PhD
Field
Political Science, Social Science
Location
Munich, Germany
Published
Oct 3, 2026
Deadline
October 20, 2026

Summary

Ludwig-Maximilians-Universität München (LMU Munich) is inviting applications for a part-time (75%, 30 hours per week) PhD Researcher position in Computational Text Analysis and Democratic Belief Systems within the Geschwister-Scholl-Institute for Political Science. The position starts on January 1, 2027, or as soon as possible thereafter, for a duration of 4 years with the possibility of extension. Remuneration is graded at TV-L E 13 (75%) and funded by the Deutsche Forschungsgemeinschaft (DFG). The candidate will conduct research in the Digitalization and Political Behavior unit led by Prof. Dr. Alexander Wuttke, contributing to projects on democratic persuasion and AI-based conversational interviewing. Applications do not specify a closing deadline in the advertisement text.

Key Facts

DepartmentGeschwister-Scholl-Institute for Political Science, Unit: Digitalization and Political Behavior
Position TypePhD Researcher / Doctoral candidate
DisciplinePolitical Science / Computational Social Science
Research AreaComputational Text Analysis and Democratic Belief Systems
SalaryTV-L E 13 (75%)
FundingDeutsche Forschungsgemeinschaft (DFG – German Research Foundation)
Contract Duration4 years (with possibility of extension)
Employer AddressMunich (München), Germany
Employer ContactProf. Dr. Alexander Wuttke

About the Project

The researcher will contribute to DFG-funded research projects, including 'Democratic Persuasion' and the Emmy Noether Research Group 'Predictably Paradoxical: Mapping the Democratic Mind with AI'. The research focuses on using computational methods, survey experiments, and large language models as semi-structured interviewers to investigate democratic belief systems, political attitudes, and communicative strategies to reinforce democratic commitment.

Key Responsibilities

  • Contribute to framed field experiment design, implementation, and data collection in collaboration with civil society partners
  • Develop and evaluate theory-driven interventions drawing on psychological and democratic theory
  • Conduct and analyze persuasive conversations using LLM-based conversation systems
  • Design, manage, and analyze survey experiments
  • Analyze experimental results and write research publications

Who Should Apply

Candidates interested in applying computational text analysis, large language models, and computational social science methods to political psychology, democratic attitudes, and public opinion should consider applying.

Benefits

  • Part-time position (75%, approx. 30 hours per week) with TV-L E 13 salary grade
  • Family-friendly employer supporting work-life balance

About the Employer

Ludwig-Maximilians-Universität München (LMU Munich), founded in 1472, is a research university in Munich, Germany, home to approximately 50,000 students and over 4,000 faculty and researchers. The Faculty of Social Sciences and its Geschwister-Scholl-Institute for Political Science focus on empirical and theoretical political science, democratic values, and interdisciplinary social science research.

Frequently Asked Questions

What is the start date for this PhD position?

The expected start date is January 1, 2027, or as soon as possible.

What is the working time percentage and salary grade?

The role is part-time at 75% (approximately 30 hours per week) and paid according to the TV-L E 13 (75%) salary grade.

How long is the employment contract?

The contract duration is 4 years, with a possibility of extension.

Who funds the underlying research projects?

The position and associated projects are funded by the Deutsche Forschungsgemeinschaft (DFG – German Research Foundation).

This listing is summarised from the official advertisement. Always confirm the details, including the closing date, on the employer's own site before applying.