Job Description

Department of Mathematical Modeling and Machine Learning

Postdoctoral Researcher in Machine Learning/NLP for Media Monitoring 90 %

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Start of employment 01.10.2025 (or as soon as possible thereafter), temporary
The Quantitative Network Science group at the Department of Mathematical Modeling and Machine Learning (DM3L) of the University of Zurich (UZH) invites applications for a Postdoctoral Researcher position to join our team. The initial appointment will be for two years.



The appointed researcher will join a DIZH-funded project developing digital tools for media monitoring and discourse analysis. In collaboration with journalism and linguistics experts at ZHAW and practice-oriented media partners, the project combines journalism, complex systems theory, applied linguistics, and machine learning to analyze how different perspectives (e.g., scientific, political, legal, moral, economical) are linked in media coverage of issues like climate change, migration, energy or political voting in Switzerland. The goal is to measure and visualize how journalists frame complex topics.

Your responsibilities

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Key objectives include:

Develop and test machine learning methods (e.g., topic modeling, LLMs) to detect journalistic perspectives in large datasets of news and social media. Collaborate closely with researchers in journalism and applied linguistics to identify systemic viewpoints in media coverage. Co-develop with media practice partners a prototype dashboard for integration into media monitoring tools.

This position is ideal for an early-career researcher aiming to advance their academic career with an interest in applications and collaborations with the private sector. The role also offers career development opportunities, including mentoring and gaining demonstrable experience and skills in teaching, research supervision, and securing research funding.



Your responsibilities:

Lead and publish original research aligned with the project's objectives. Develop the web dashboard with the rest of the project's team. Contribute to the teaching and supervision of students at the DM3L. Actively participate in and contribute to Quantitative Network Research group research activities. Contribute to project funding applications within the group and apply for personal research funding (e.g., postdoctoral fellowships and grants).

Your profile

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A completed PhD degree in a related field. Experience with peer-reviewed publications in international journals. Excellent organizational and communication skills. Strong programming and analytical skills, committed to open and reproducible research practices. Experience with machine learning/NLP approaches for analyzing large amount of textual data with a willingness to further develop these skills in the project context. Strong interpersonal and team collaboration skills. Fluency in spoken and written English. Knowledge of German is not required but would be an asset. Opportunities to learn German will be available during the appointment.


What will help you thrive in this role:

Keen interest in critically evaluating machine learning methods and the integration into media monitoring and discourse analysis challenges Enthusiasm for interdisciplinary research and learning new methods Passionate about teaching and supervision Team-oriented and open to diverse backgrounds and perspectives Creative and analytical problem-solver

Information on your application

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To find out more about the research undertaken at the Quantitative Network Science group, visit our webpage.

For any inquiries about the position, please get in touch with Prof. Alexandre Bovet at dizh_postdoc@proton.me.



To apply, please upload the following documents in English to our Job Portal:A motivation letter explaining your interest in the position, how you meet the criteria, how you envision contributing to the research project, and how it aligns with your career goals. A CV, including a list of publications. Arrange two referees who agreed to be contacted Degree transcripts. An example of a significant research contribution you have made (e.g., a scientific paper, piece of software), along with a 300-500-word summary outlining why you consider this contribution significant and describing your role in the research. Please also upload the original research contribution or paper.


The initial deadline for applications is July 12, 2025. Review of applications will commence thereafter and continue until the position is filled.

What we offer

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Work-Life Balance

Flexible working models (such as part-time positions, mobile working, job-sharing) Childcare at the kihz foundation of UZH and ETH

Learning and Development

Wide range of continuing education courses of UZH and the Canton of Zurich Language Center run jointly with ETH Zurich

Food

Food and drinks at reduced prices in the UZH cafeterias Lunch-Check-card with UZH contribution

Healthcare

Special conditions on the Academic Sports Association ASVZ Free seasonal flu vaccinations Rest and relaxation at the quiet room in the university tower

Discounts

Private traffic: Carsharing, rent a vehicle, parking space Digitalization: Hardware, software, mobile phone subscriptions Special conditions on hotel reservations

Conditions of Employment

Policies of the UZH Most UZH staff are employed according to public law

International Services

Support for people from outside Switzerland

Campuses

Campuses Zurich City, Zurich Irchel, Oerlikon and Schlieren Sites Zurich West, Old Botanical Garden, Botanical Garden and Lengg

Location

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Department of Mathematical Modeling and Machine Learning

Winterthurerstrasse 190, 8057 Zurich, Switzerland

Further information

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Questions about the job

Alexandre Bovet

Professor

Working at UZH

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The University of Zurich, Switzerland's largest university, offers a range of attractive positions in various subject areas and professional fields. With around 10,000 employees and currently 12 professional apprenticeship streams the University offers an inspiring working environment on cutting-edge research and top-class education. Put your talent and skills to work with us. Find out more about UZH as an employer!