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University of Bern

PhD in Statistics: Conditional Generative Modelling of Local High-Impact Climate Events University of Bern in Switzerland

Degree Level

PhD

Field of study

Computer Science

Funding

PhD position, 100% employment for 4 years. The position is part of the NCCR CLIM+ programme funded by the Swiss National Science Foundation (SNSF).

Deadline

Oct 1, 2026

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Country

Switzerland

University

University of Bern

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Keywords

Computer Science
Environmental Science
Mathematics
Hydrology
Earth Science
Spatial Statistics
Generative Modeling
Numerical Weather Prediction
Climate Extremes
Statistics

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About this position

PhD opportunity at the University of Bern in Statistics, focused on conditional generative modelling of local high-impact climate events under structured scenarios. The project sits at the intersection of statistics, computer science, environmental science, and earth science, with strong links to generative machine learning, weather forecasting, climate research, and downscaling of global predictions to local scales.

The PhD student will be hosted at the Institute of Mathematical Statistics and Actuarial Science (IMSV) in the Uncertainty Quantification and Spatial Statistics group led by Prof. David Ginsbourger. The project investigates new conditional generative modelling approaches for studying future high-impact events under prescribed conditioning scenarios, including large-scale dynamical storylines and event-prone regimes. Methodological topics include conditional generation, integrating heterogeneous data sources, representing conditioning information, and generating plausible local outcomes under changing and potentially unprecedented climate conditions.

The position is part of the NCCR CLIM+ programme on Climate Extremes and Society, funded by the Swiss National Science Foundation (SNSF). The broader collaboration includes researchers at ETH Zurich, WSL, MeteoSwiss, EPFL Valais Wallis, and the University of Bern, with possible work on downscaling and debiasing input data for hydrological models.

Eligibility highlights: MSc in statistics or a closely related field, strong mathematical background, strong programming skills (ideally Python and/or R), knowledge or experience in generative machine learning, experience with large datasets such as hydrological, meteorological, or climate observations/simulations, and very good English communication skills.

Funding: 100% PhD employment for 4 years. Deadline: applications submitted by 2026-10-01 receive full consideration. Start date is November 1, 2026, or by arrangement.

Funding details

PhD position, 100% employment for 4 years. The position is part of the NCCR CLIM+ programme funded by the Swiss National Science Foundation (SNSF).

What's required

Applicants should have an MSc degree in statistics or a closely related field with a strong mathematical background. Strong programming skills, ideally in Python and/or R, are required. Knowledge and ideally practical experience of generative machine learning is expected. Experience working with large datasets, ideally hydrological, meteorological, or climate observations/simulations, is preferred. Very good oral and written English communication skills and a collaborative, interdisciplinary mindset are required.

How to apply

Prepare a single PDF application including CV, cover letter, diploma certificates and transcripts, a link to your MSc thesis, and contact details of two professional references. Submit it online by 2026-10-01 to the University of Bern application portal or send as instructed to [email protected].

More information can be found here

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