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

University of Bern; Bern; Vor Ort; CHF 42.000 - 54.000; Vollzeit

Zusammenfassung

The University of Bern invites applications for a PhD position hosted at the Institute of Mathematical Statistics and Actuarial Science (IMSV) as part of the NCCR CLIM+ programme. The successful candidate will join the Uncertainty Quantification and Spatial Statistics group led by Prof.

David Ginsbourger and contribute to climate risk research. The project focuses on Local Risks: Impacts and Adaptation, including downscaling and debiasing input data for hydrological models, with regular

Qualifikationen

• MSc in statistics or a closely related field with a strong mathematical background.
• Strong programming skills in Python and/or R.
• Knowledge and ideally practical experience of generative machine learning.
• Experience working with large datasets, ideally hydrological, meteorological or climate data.
• Very good oral and written communication skills in English.
• Motivation to work in an interdisciplinary and international environment with a collaborative mindset.

Aufgaben

• Contribute to the Local Risks: Impacts and Adaptation work package of the NCCR CLIM+ programme.
• Collaborate with teams at ETH Zurich, MeteoSwiss, EPFL and University of Bern.
• Develop methods for downscaling and debiasing input data for hydrological models.
• Participate in interdisciplinary meetings, seminars and publications.

Kenntnisse

Python

R

Generative ML

Ausbildung

MSc in statistics

Jobbeschreibung

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Research and Collaborative Environment

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.

Profile of the candidate

• 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