Universität Bern · Education
PhD on Conditional Generative Modelling of Local High-Impact Events under Structured Scenarios
UniBE is ambitious.
With us, you deliver top performance and test your skills against the best. The PhD student will be hosted at the Institute of Mathematical Statistics and Actuarial Science (IMSV) of the University of Bern, joining the Uncertainty Quantification and Spatial Statistics group led by Prof. David Ginsbourger. The IMSV is an engaged, open-minded institute where mathematical statistics and probability theory meet data and models arising from various scientific and societal challenges
The position is part of the NCCR CLIM+ programme on Climate Extremes and Society, funded by the Swiss National Science Foundation (SNSF). More specifically, the PhD project will contribute to the work package “Local Risks: Impacts and Adaptation” and involve participation in tasks covering topics such as the downscaling and debiasing of input data for hydrological models. Regular interactions and potential collaborations with the teams of Prof. Manuela Brunner (ETH Zurich, WSL), Dr. Christian Grams (MeteoSwiss), Prof. Michael Lehning (EPFL Valais Wallis, WSL), and Prof. Olivia Romppainen-Martius (University of Bern) are planned.
The NCCR CLIM+ supports Switzerland in the transformation towards a climate-resilient society. Together with stakeholders, the interdisciplinary NCCR CLIM+ research community tackles unexplored solution spaces to the climate crisis and develops a blueprint for actionable climate research worldwide. NCCR CLIM+ broadly communicates and shares knowledge, and trains a new generation of experts with the necessary domain and transdisciplinary knowledge.
The NCCR CLIM+ strives to implement equal opportunity at hiring. At NCCR CLIM+ we believe that diversity of thought, background and experience creates better research. • A MSc degree in statistics or a closely related field with strong mathematical background
• Strong programming skills (ideally in Python and/or R)
• Knowledge and ideally practical experience of generative machine learning
• Experience in working with large datasets, ideally hydrological, meteorological or climate observations/simulations
• Very good oral and written communication skills in English
• Motivation to work in an interdisciplinary and international working environment and a collaborative mindset International reputation
With us, you deliver top performance and test your skills against the best. The PhD student will be hosted at the Institute of Mathematical Statistics and Actuarial Science (IMSV) of the University of Bern, joining the Uncertainty Quantification and Spatial Statistics group led by Prof. David Ginsbourger. The IMSV is an engaged, open-minded institute where mathematical statistics and probability theory meet data and models arising from various scientific and societal challenges
The position is part of the NCCR CLIM+ programme on Climate Extremes and Society, funded by the Swiss National Science Foundation (SNSF). More specifically, the PhD project will contribute to the work package “Local Risks: Impacts and Adaptation” and involve participation in tasks covering topics such as the downscaling and debiasing of input data for hydrological models. Regular interactions and potential collaborations with the teams of Prof. Manuela Brunner (ETH Zurich, WSL), Dr. Christian Grams (MeteoSwiss), Prof. Michael Lehning (EPFL Valais Wallis, WSL), and Prof. Olivia Romppainen-Martius (University of Bern) are planned.
The NCCR CLIM+ supports Switzerland in the transformation towards a climate-resilient society. Together with stakeholders, the interdisciplinary NCCR CLIM+ research community tackles unexplored solution spaces to the climate crisis and develops a blueprint for actionable climate research worldwide. NCCR CLIM+ broadly communicates and shares knowledge, and trains a new generation of experts with the necessary domain and transdisciplinary knowledge.
The NCCR CLIM+ strives to implement equal opportunity at hiring. At NCCR CLIM+ we believe that diversity of thought, background and experience creates better research. • A MSc degree in statistics or a closely related field with strong mathematical background
• Strong programming skills (ideally in Python and/or R)
• Knowledge and ideally practical experience of generative machine learning
• Experience in working with large datasets, ideally hydrological, meteorological or climate observations/simulations
• Very good oral and written communication skills in English
• Motivation to work in an interdisciplinary and international working environment and a collaborative mindset International reputation