# Student Research Assistant, Team Transparent Social Analytics (SHK-CSS-39) at Gesis

> Germany · Unspecified · Analytics / Data science · open. Source: https://alderhq.app/jobs/personio%3Agesis%3A2851058 (read 2026-11-08).

## Facts (observed)

- Company: [Gesis](https://alderhq.app/companies/gesis)
- Location text: Köln
- Where it can be done from (inferred): Germany (evidence: "Location: Köln")
- Posted: 2026-10-20 (19 days ago)
- First seen by alder: 2026-11-04 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://gesis.jobs.personio.de/job/2851058
- Skills listed (inferred): CSS, Python, LLMs

## Pay and sponsorship

- Posted pay: €32k–€32k per year (from the description)

## Description (the employer's text)

Starting as soon as possible, our Department Computational Social Science (CSS), Team Transparent Social Analytics located in Cologne, is looking for a

### Student Research Assistant

### (15,20 € / 16,09 € hourly rate, up to 10 hours per week, temporary)

The department Computational Social Science (CSS) collects digital behavioral data and provides computer-based methods for collecting and analyzing such data for social science research. It also supports scientists with the integration of digital behavioral data into their research designs. The department's research focuses on the quality of digital behavioral data, the development and validation of computational social science methods and the transformation of digital societies.

The BMFTR-funded project WIEGE (“Wiederholt Getäuscht” - “Repeatedly Deceived”) investigates how repeated, cross-platform political disinformation shapes young people’s everyday media experiences and what consequences it has for their attitudes, trust, and behavior. In the Transparent Social Analytics team, we are working on working packages within WIEGE that are concerned with the automated detection of disinformation in social media data. To this end, we compare various machine learning-based methods. We also use computational methods to analyze the content of cross-platform political disinformation.

### Your tasks will be:

- Systematic evaluation of research literature in the fields of social sciences, computational social science, and data science
- Assistance in developing pipelines for the automated detection of disinformation in large text corpora using established machine learning models and LLMs
- If necessary, assistance with the manual annotation of smaller datasets
### Your profile:

- Enrollment in a bachelor's (advanced) or master's program in sociology, political science, communication studies, linguistics, computer science, data science, or a related discipline
- Proficiency in Python, or proficiency in R with a willingness to learn Python
- Proficiency in German and English
- Experience in systematically analyzing extensive research literature, or a strong willingness to learn
- Experience in applying natural language processing methods to large datasets, or a strong willingness to learn
- Interest in addressing social science questions using computational methods (Computational Social Science)
### Our Benefits:

- Exciting and practical insights into an interdisciplinary research project at the intersection of the social sciences and computer science
- An interdisciplinary work environment with a friendly and collegial atmosphere, as well as a wide range of opportunities to develop your professional and methodological skills, including the opportunity to write a thesis related to the project
- Flexible working hours and regulations for mobile working
- Very good conditions for reconciling work and family life
- Holistic company health management and discounted participation in the university's sports programme
- Promotion of your skills through further training measures
### Contact

For further information concerning the tasks please contact Pia Schrot via E-Mail. If you have questions about the application process, please contact Christine Laßmann via E-Mail.

### Interested?

Our reference number is: SHK-CSS-39

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/personio%3Agesis%3A2851058 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.