Predicting Mechanical Strength Professors in Global

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Pedro Reis
École Polytechnique Fédérale de Lausanne Switzerland
mechanics
mechanical instabilities
experimental mechanics
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Filip Schlembach
Maastricht University Netherlands
conformal prediction
predictive uncertainty quantification
time series forecasts
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Edoardo Mazza
ETH Zurich Switzerland
material damage prediction
mechanical characterization of structures
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Stefan Hergarten
University of Freiburg Germany
risk prediction
hazard prediction
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Michael Sternberg
Imperial College London United Kingdom
function prediction
protein structure prediction
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Davide Leonetti
Eindhoven University of Technology Belgium
strength assessment
mechanical performance of steel structures
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Nikhil Admal
University of Illinois United States of America
material behavior prediction
mechanical properties
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Peter Hosemann
University of California, Berkeley United States of America
mechanical performance
multi-scale mechanical property quantification
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Dale Griffin
University of British Columbia Canada
prediction
forecasting
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Yves Weinand
École Polytechnique Fédérale de Lausanne Switzerland
mechanical characterization
structural mechanics
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Fredrik Lindsten
Linköping University Sweden
statistical predictions
weather forecasting
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Sumanta Basu
Cornell University Netherlands
prediction
statistical machine learning
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Burkhard Rost
Technical University of Munich Germany
protein structure prediction
gene structure prediction
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Emma Davis
University of Warwick United Kingdom
mechanistic modelling
forecasting
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Martyn Clark
University of Calgary Canada
environmental prediction
earth system prediction
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Sebastian Fossati
University of Alberta Canada
prediction
time series forecasting
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André Bégin-drolet
Laval University Canada
mechanical characterization
mechanical design
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Rina Foygel Barber
University of Chicago United States of America
prediction
conformal prediction
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Nedjeljka Žagar
University of Hamburg Germany
predictability
numerical weather prediction
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Tilda Herrgårdh
Linköping University Sweden
stroke prediction
mechanistic models
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Julian Kooij
Delft University of Technology Netherlands
trajectory forecasting
statistical machine learning
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