PhD on Conditional Generative Modelling of Local High-Impact Events under Structured Scenarios

The University of Bern’s Institute of Mathematical Statistics and Actuarial Science (IMSV) is looking for a PhD candidate to advance conditional generative modelling of local high‑impact events. The position sits within the NCCR CLIM⁺ programme, a Swiss National Science Foundation‑funded effort that supports climate‑resilient research and brings together experts from several universities and research institutes. During the doctorate you will focus on the “Local Risks: Impacts and Adaptation” work package. Core tasks include developing and applying down‑scaling and debiasing techniques for input data used in hydrological models, and building conditional generative models under structured scenarios. You will regularly interact with teams from ETH Zurich, MeteoSwiss, EPFL Valais‑Wallis and other partners, contributing to interdisciplinary projects and sharing results within the NCCR CLIM⁺ community. The ideal candidate holds an MSc in statistics or a closely related discipline and possesses a solid mathematical foundation. Strong coding abilities—preferably in Python or R—are required, together with a good grasp of generative machine‑learning concepts. Experience handling large data collections, especially those related to hydrology, meteorology or climate simulations, is essential. Excellent written and spoken English and a clear motivation to work in a collaborative, international setting are also mandatory. The role offers a vibrant research environment at the University of Bern, with access to a broad network of climate scientists and statisticians. NCCR CLIM⁺ promotes equal opportunity and values diverse perspectives, providing a supportive atmosphere for personal and professional growth. What the role asks for: - MSc in statistics or related field - Strong mathematical background - Strong programming skills (Python/R) - Knowledge of generative machine learning - Experience with large datasets - Very good English oral and written communication - Motivation for interdisciplinary, international collaboration - Practical experience with generative machine learning - Experience with hydrological, meteorological or climate data - Interest in climate‑resilience research - Ability to work with NCCR CLIM+ partners