Doctoral Research Position with Focus on Automated Text Analysis and Computational Social Science

26.02.2024, Wissenschaftliches Personal

The Emmy Noether Junior Research Group "The media portrayal of majority and minority groups" (Stefanie Walter, PhD) invites applications for a 3-year doctoral research position (65%) with focus on automated text analysis and computational social science staring between June – September 2024. The position is based in the Department of Governance in TUM School of Social Sciences and Technology, at the Technical University of Munich.

About Us

The applicant will be a core member of the Emmy Noether Junior Research Group "The media portrayal of majority and minority groups". The group is funded by the German Research Foundation (DFG) and led at TUM by Stefanie Walter, PhD. It aims at analyzing similarities and differences in the media portrayal of majority and minority groups by studying them comparatively: The project aims to reveal similarities and differences in the media portrayal of majority and minoritized groups by studying them comparatively. It examines changes over time, across countries and news outlets, whilst tak-ing explanatory factors related to minoritized and majority group coverage into account. The main news media sample includes data from Germany, Poland, the UK, and the US over a 20-year period. In terms of group representation, the project is especially interested in the news coverage of gender, as well as sexual, ethnic and religious minoritized groups.

Requirements

• Master’s degree in Political Science, Media and Communication, or related disciplines.
• Skills in quantitative methods and automated text analysis / computational social science using large data sets (e.g., documented in transcripts, further training, or course work).
• Strong command of R (and/or Python) with experience using text analysis packages such as quanteda or tidytext (e.g., documented in transcripts, further training, or course work).
• Basic understanding of machine learning tools, word embeddings, support vector machines, LLMs, neural networks is a plus.
• Experience with web scraping, databases, and APIs is desirable.
• Interest in political communication (e.g., documented in transcripts, further training, or course work).
• Strong ability to communicate in spoken and written English (required; Level B2 or equivalent) e.g., documented by university entrance diploma or English language certificate).

Tasks

This position will focus on the quantitative componentwith an emphasis on automated text analysis and computational social science. The doctoral researcher will be actively involved in the project’s research activities, including the design and implementation of studies. They will also be expected to help co-author articles based on this project for publication and have the opportunity to pursue a doctoral degree.

Our offer

This is a 3-year research position with the opportunity to pursue a doctoral degree at the Technical University of Munich, one of Germany's most highly ranked research universities (in residence). The starting date is flexible, between June 1st and September 1st ; please state your earliest possible starting date within the application. Remuneration will be in accordance with the German public service pay scale (collective agreement for state-level public servants, TV-L) at the E-13 level (65%). An exciting research environment with international collaborations and ongoing cutting-edge research projects, funding support for conferences and research activities.

The project strongly supports the TUM's strategic goal of increasing the diversity of its staff. As an equal opportunity and affirmative action employer, TUM is striving to increase the proportion of women, so applications from women are expressly welcomed. We also particularly encourage applications from people with disabilities, members of groups that are traditionally underrepresented in the academy, and others who would increase the diversity of the university's research and teaching staff.

The position is suitable for people with disabilities. People with disabilities applying for this position will be given preference if they otherwise have essentially the same suitability, ability and professional performance.

Application

To apply, please send the following materials electronically and as a single pdf file to: [email protected]

• Cover letter (no longer than 2 pages) that explains your interest in the project and details your experience with quantitative methods, automated text analysis/computational social science. Please include your reasons for applying for this particular position and your ideal starting date.
• Current curriculum vitae (CV).
• A copy of your academic certificates (if available, transcripts providing specific information about your university-level coursework and/or a list of courses that is informative about your substantive and methodological preparation).
• A writing sample (research paper, publication, or Master’s thesis).
• Contact details of two referees who can attest to your research and technical abilities.

All applications received by 25th March 2024 will be considered. Please do not hesitate to contact Alice Beazer ([email protected]) if you have any questions.

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.

Kontakt: [email protected]