Job Description

We invite applications for a highly qualified postdoctoral researcher to join the interdisciplinary project "Transparency in Lobbying through AI". The project aims to lay the technical and conceptual foundations for a future large-scale initiative that will develop next-generation methods and tools for understanding political influence networks.

The position offers the opportunity to conduct cutting-edge research at the intersection of artificial intelligence, graph-based knowledge representation, political data analytics, and democratic innovation.

About the Institution

The Institute of Computer Science at the University of Bern conducts internationally recognized research in diverse fields of computer science. The successful candidate will be hosted in the Pattern Recognition Group, which specializes in graph-based machine learning, interactive AI systems, and retrieval-augmented language models.

The project is jointly led with the E-Democracy Research Group at the KPM Center for Public Management, known for its research on democratic processes, digital participation, and political behaviour.

About the Project (BIND)

The preparatory BIND project develops the foundations for a large-scale research effort aimed at bringing lobbying transparency in Switzerland to a new level. Its core elements include: automated data acquisition from heterogeneous political sources, graph-based modelling of influence networks, integration of retrieval-augmented large language models, natural language interfaces for non-expert users, and explainable AI mechanisms



The postdoc will play a central role in designing data pipelines, prototyping the knowledge graph and RAG architecture, and contributing to an interactive demonstrator for journalists, researchers, and citizens.


Tasks
Lead the technical development, including:

Designing web-scraping and data ingestion pipelines for lobbying-related sources.

Building and evaluating graph-based knowledge representations.

Developing and experimenting with retrieval-augmented LLM architectures.

Prototyping interactive user interfaces and explainability components.

Collaborate closely with political science partners on interdisciplinary research questions.

Co-author scientific publications based on project results.

Contribute to the preparation of the SNSF principal project proposal.
Requirements


PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computational Social Science, or a closely related field.

Strong expertise in at least two of the following areas:

NLP and LLMs (incl. fine-tuning and RAG systems)

Graph databases and knowledge graph modelling

Web scraping, data extraction, and data engineering

Explainable AI

Interactive systems or data visualisation

Excellent programming skills (Python preferred).

Ability to work independently and in an interdisciplinary team.

Strong scientific writing skills and publication record.

Interest in civic technology, political data, or democratic processes is an advantage.
We offer


A stimulating interdisciplinary research environment in two leading research groups at the University of Bern.

Close collaboration with experts in political science and public management.

Access to state-of-the-art computing infrastructure.

Opportunity to contribute to high-impact research relevant for democratic transparency.

Funding to attend one international conference.

A supportive, collaborative team environment and opportunities for academic advancement.
Please send a single PDF containing:a cover letter, CV with publication list, contact information of two references, and a short statement of research interests relevant to the project.

Applications should be sent via email to:

PD Dr. Kaspar Riesen - kaspar.riesen@unibe.ch
Applications received by January 31 2026 will receive full consideration.

The position will remain open until filled.





www.karriere.unibe.ch
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