معرفی
Sebastian Schemm is a Heisenberg Fellow at the Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, a position regarded as equivalent to a non-permanent Associate Professor. He leads research within the Atmosphere-Ocean Dynamics group and previously held an ERC Starting Grant-funded Assistant Professorship (without tenure track) at ETH Zurich.
Education and Career Path
- PhD (2013) and MSc (2010), ETH Zurich, Switzerland
- Postdoctoral researcher, University of Bergen, Norway (2014–2017)
- Postdoctoral researcher, Laboratoire de Météorologie Dynamique, ENS Paris (2017–2018)
- Assistant Professor (ERC Starting Grant), ETH Zurich (2020–2024)
- Heisenberg Fellow, DAMTP, University of Cambridge (2025–present)
Research Focus
Schemm’s work centres on atmospheric and climate dynamics, spanning turbulence to planetary scales. Core themes include the physics of extratropical cyclone life cycles, jet-stream and storm-track dynamics, Rossby waves and teleconnection patterns, high-resolution atmospheric modelling, and the integration of machine-learning techniques for parameter estimation, data assimilation, and kilometre-scale global simulations. He also contributes to large-scale initiatives such as ECMWF’s WeatherGenerator.
Scientific Awards and Editorial Service
- DFG Heisenberg Fellowship (2025)
- ERC Starting Grant (2020–2024)
- European Meteorological Society Young Researcher Medal (2019)
- Co-Editor, Weather and Climate Dynamics (EGU)
- Co-Editor, Quarterly Journal of the Royal Meteorological Society
PhD Supervision & Funding
He currently supervises PhD students at both Cambridge and ETH Zurich, with funding streams including the Cambridge CREATES Doctoral Training Partnership and Swiss/EU grants. Ongoing students explore reinforcement-learning parameterisations, jet-stream–storm-track relationships, mid-latitude eddy energetics, machine-learning ensemble forecasting, and Bayesian parameter estimation in LES.
Active Projects
- EU Horizon project WeatherGenerator (led by ECMWF)
- PASC HiRAD-Gen: High-Resolution Atmospheric Downscaling Using Generative Models
