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Peter Dueben
University Research Fellow of the Royal Society at ECMWF
Coordinator of AI and Machine Learning activities
Description
Peter is the AI and Machine Learning Coordinator at ECMWF and holds a University Research Fellowship of the Royal Society that enables him to perform research towards the use of machine learning, high-performance computing, and reduced numerical precision in weather and climate predictions. Peter has also a strong interest in the quantification of uncertainty of predictions for chaotic systems. Peter is coordinator of the MAELSTROM EuroHPC project, work-package leader of the ESiWACE2 H2020 project, and Co-Pi of an US-INCITE grant to perform season-long, global, storm-resolving simulations. Before moving to ECMWF, Peter has written his PhD thesis at the Max Planck Institute for Meteorology and has worked as PostDoc with Tim Palmer at the University of Oxford.
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