Machine Learning And Physics Professors in Global

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Rudolf Römer
University of Warwick United Kingdom
mathematical physics
computational physics
meso- and nanoscopic physics
biological physics
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Gordon Watts
University of Washington United States of America
computational physics
machine learning
particle physics
nuclear physics
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Jeffrey Schenker
Michigan State University United States of America
mathematical physics
theoretical physics
statistical physics
quantum mechanics
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Peter Mcmahon
Cornell University Netherlands
machine learning
material physics
statistical learning
semiconductor physics
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Charles Hoyle
University of Washington United States of America
theoretical physics
experimental physics
particle physics
quantum mechanics
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Simone Loreti
University of Bern Switzerland
computational physics
mathematical modelling
engineering physics
physics
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Jan Koster
University of Groningen Netherlands
materials physics
quantum physics
physics
nano physics
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Victor Flambaum
University of New South Wales Australia
theoretical physics
particle physics
nuclear physics
quantum mechanics
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Phiala Shanahan
Massachusetts Institute of Technology United States of America
machine learning in physics
particle physics
nuclear physics
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Jessica Sorrell
Johns Hopkins University United States of America
machine learning algorithms
machine learning
machine learning theory
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Somnath Ghosh
Johns Hopkins University United States of America
multi-physics modeling
machine learning
physics
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Oliver Schlotterer
Uppsala University Sweden
mathematical physics
geometry and physics
theoretical physics
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David Brückner
University of Basel Switzerland
machine learning
theoretical physics
statistical physics
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Duncan O'dell
McMaster University Canada
mathematical physics
quantum physics
molecular physics
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Martin Kröger
ETH Zurich Switzerland
computational physics
theoretical physics
statistical physics
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Ambuj Tewari
University of Michigan United States of America
theory of machine learning
machine learning
reinforcement learning
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Rickard Armiento
Linköping University Sweden
machine learning
theoretical physics
physical modeling
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Tal Kachman
Radboud University Netherlands
machine learning
statistical physics
reinforcement learning
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Niels Walet
University of Manchester United Kingdom
computational physics
theoretical physics
nuclear physics
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Yuxin Chen
University of Pennsylvania United States of America
machine learning theory
statistical machine learning
reinforcement learning
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Vinay Gogineni
University of Southern Denmark Denmark
machine learning
quantum machine learning
physics-informed neural networks
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