Tom Beucler
Assistant Professor In Environmental Data Science
Lausanne Metropolitan Area Switzerland
Summary
👤
Senior🎓
Top SchoolTom Beucler is an Assistant Professor in Environmental Data Science at the University of Lausanne, where he leads the ∂AWN lab applying machine learning to atmospheric physics to advance weather and climate prediction. With nine years of research experience and a PhD from MIT, he bridges fluid dynamics, meteorology, and data-driven methods to tackle problems in atmospheric dynamics. His career includes postdoctoral and research scientist roles at Columbia and UC Irvine, reflecting strong interdisciplinary training and international collaboration. Tom combines rigorous theoretical grounding from École Polytechnique with practical ML-driven modeling, developing tools that improve process-level understanding rather than just forecast skill. He is based in the Lausanne metropolitan area and is notable for steering a university lab that integrates cutting-edge ML with domain physics—emphasizing interpretable, physically consistent solutions.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Atmospheric Sciences and Meteorology, Doctor of Philosophy (Ph.D.), Atmospheric Sciences and Meteorology at Massachusetts Institute of TechnologyMechanics, Physics, Mathematics, Chemistry, Mechanics, Physics, Mathematics, Chemistry at Lycée Sainte-GenevièveEngineer's degree, Fluid Dynamics, Environmental Science, Mechanics, Physics, Engineer's degree, Fluid Dynamics, Environmental Science, Mechanics, Physics at Ecole polytechnique
French, German, English, Spanish
Github Skills (9)
parameterization10
machine-learning7
deep-learning7
neural-network5
jekyll3
speaker3
jekyll-theme3
modeling2
python2
Programming languages (4)
Github contributions (5)
tbeucler/2022_ML_Earth_Env_Sci
Feb 2022 - Apr 2022
Contributions:60 commits, 53 pushes, 1 branch in 1 month
tbeucler/CBRAIN-CAM
Jan 2019 - Sep 2022
Code for neural network parameterization project
Contributions:2 releases, 288 commits, 287 pushes in 3 years 8 months
parameterizationdeep-learningneural-networksmachine-learningneural-network
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