Machine Learning Applications In Geoenergy Professors in Global

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Jamal Arsanjani
Aalborg University Denmark
geoinformatics
geospatial modelling
machine learning
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Gioia Falcone
University of Glasgow United Kingdom
geo-energy engineering
geothermal engineering
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Suzanne Hangx
Utrecht University Netherlands
geo-energy
geomechanics
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Brice Lecampion
École Polytechnique Fédérale de Lausanne Switzerland
geo-energy
geomechanics
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Guotao Ma
University of Warwick United Kingdom
geoengineering
machine learning
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Sheng Dai
Georgia Institute of Technology United States of America
energy geotechnics
geomechanics
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Juliana Leung
University of Alberta Canada
geo-energy
machine learning
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Vanessa Di Battista
University of Sherbrooke Canada
geoenvironmental engineering
geosynthetics
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Urs Hugentobler
Technical University of Munich Germany
geodynamic applications
precision geodetic applications
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Michael Golombok
Eindhoven University of Technology Belgium
energy geoscience
geothermal engineering
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Christoph Bärlocher
ETH Zurich Switzerland
applied geophysics
mathematical geophysics
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Mike Kendall
University of Oxford United Kingdom
applied geophysics
environmental geophysics
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Lindsey Heagy
University of British Columbia Canada
applied geophysics
machine learning
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Marc Hesse
University of Texas at Austin United States of America
computational geoscience
geomechanics
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Mohammad Rezania
University of Warwick United Kingdom
modeling of geostructures
geomechanics
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Alison Heppenstall
University of Glasgow United Kingdom
geocomputation
machine learning
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Ross Purves
University of Zurich Switzerland
geocomputation
machine learning
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Esmaeil Nadimi
University of Southern Denmark Denmark
energy applications
machine learning
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Mehrdad Pirnia
University of Waterloo Canada
energy applications
machine learning
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Seán Kavanagh
University of Cambridge United Kingdom
energy applications
machine learning
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Anthea Monod
Imperial College London United Kingdom
geometric machine learning
machine learning
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