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Didier Wernli

4 months ago

Postdoctoral Researcher in Modeling/Natural Language Processing (NLP)/Data Science for Global Governance University of Geneva in Switzerland

Degree Level

Postdoc

Field of study

Computer Science

Funding

Available

Deadline

Expired

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Country

Switzerland

University

University of Geneva

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Keywords

Computer Science
Data Science
Information Technology
Mathematics
Political Science
International Relations
Public Policy
Natural Language Processing
Agent-based Modeling
Bayesian Statistics
Topic Modeling
Content Analysis
Network Biology
Global Governance
Transformer Technology
Computational Modelling
Social Dynamics
Shared Decision Making
Statistic
Machine learning

About this position

The Science in Diplomacy Lab (SiDLab) at the University of Geneva is seeking a postdoctoral researcher to advance interdisciplinary research at the intersection of computational modeling, natural language processing (NLP), data science, and global governance. The successful candidate will join a vibrant research environment, co-supervised by Prof.

Didier Wernli and Prof. Roland Bouffanais, and collaborate with ETH Zürich and international organizations through the SiDLab initiative. The position focuses on developing innovative computational methods to analyze, simulate, and understand complex sociopolitical phenomena such as norm diffusion, collective decision-making, and policy evolution within multilateral governance systems. Key responsibilities include designing and implementing NLP pipelines to extract insights from large-scale textual corpora, applying machine learning and network science to analyze multilateral interactions, and developing computational models (agent-based, Bayesian inference, social contagion) to study norm emergence and decision-making dynamics in diplomatic negotiations.

The role offers opportunities for close collaboration with researchers, diplomats, and policymakers, translating research findings into actionable tools for global governance, and disseminating results through high-impact journals and international forums. Applicants should have a recently completed PhD in computer science, computational social science, data science, applied mathematics, or a related field, with proficiency in Python or Julia and experience in relevant computational techniques.

Interest in international relations and global governance is expected, and fluency in English is required. The initial appointment is for one year, renewable for up to two additional years based on performance. The position provides access to UNIGE’s computational resources, mentorship in science-diplomacy interfaces, and professional development opportunities.

Applications are accepted on a rolling basis until December 15th, 2025, with an immediate start date possible. Required application materials include a CV with publication list, a 2-page research statement, and contact information for three references.

Funding details

Available

What's required

Applicants must have recently completed a PhD in computer science, computational social science, data science, applied mathematics, or a related field. Proficiency in Python or Julia for NLP, modeling, and data analysis is required. Experience with transformer-based models, topic modeling, semantic analysis, network analysis, Bayesian inference, or stochastic modeling is desirable. Interest in international relations, multilateral diplomacy, or global governance is expected. Prior experience in international relations and global governance is desirable but not required. Fluency in English is required. Applicants must submit a CV with publication list, a 2-page research statement outlining methodological approaches to norm diffusion or collective decision-making, and contact information for 3 references.

How to apply

Submit your application via the provided online portal. Include a CV with publication list, a 2-page research statement, and contact information for three references. Applications are reviewed on a rolling basis until December 15th, 2025. Early submission is encouraged.

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