Computational Methods In Proteomics Professors in Global

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Alison Barth
Carnegie Mellon University United States of America
physiological methods
molecular methods
anatomical methods
statistical methods
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Michael Szafron
University of Saskatchewan Canada
biostatistical methods
computational biomodelling
statistical methods
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Kate Hong
Carnegie Mellon University United States of America
computational methods
mathematical methods
physiological methods
anatomical methods
statistical methods
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Professorship Biometry Molecular Medicine
Ludwig Maximilian University of Munich Germany
biostatistical methods
statistical methods
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Tai Lee
Carnegie Mellon University United States of America
physiological methods
anatomical methods
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Byron Yu
Carnegie Mellon University United States of America
physiological methods
anatomical methods
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Elke Stroeher
University of Western Australia Australia
quantitative proteomics
proteomics
targeted proteomics
proteome analysis
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Khalil Sima'an
University of Amsterdam Netherlands
optimization methods
computational linguistics
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Nate Veldt
Texas A&M University United States of America
computational optimization
matrix computations
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J Okeme
McMaster University Canada
modelling methods
analytical methods
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Manex Aguirrezabal Zabaleta
University of Copenhagen Denmark
computational morphology
computational phonology
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Melik Demirel
Pennsylvania State University United States of America
computational materials science
computational analysis
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Rens Bod
University of Amsterdam Netherlands
computational linguistics
computational musicology
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Rens Bod
University of Amsterdam Netherlands
computational linguistics
computational musicology
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Rens Bod
University of Amsterdam Netherlands
computational linguistics
computational musicology
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Adrianus Van Duin
Pennsylvania State University United States of America
computational materials science
materials computation
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Derek Schipper
University of Waterloo Canada
analytical methods
synthetic methods
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Alice Richardson
Australian National University Australia
data mining methods
statistical methods for large datasets
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Eva Vrangbæk
Aarhus University Denmark
computational research
data-intensive methods
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Iris Tien
Georgia Institute of Technology United States of America
probabilistic methods
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Matthew Devos
Simon Fraser University Canada
probabilistic methods
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