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datasets. Proficiency with geometric morphometrics and image alignment. Proficiency in applying quantitative genetic methods to large datasets. Proficiency with large-scale animal models using Bayesian
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and Statistics we conduct research within the theory and implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods
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implementation of biomathematics, biostatistics, spatial modeling, differential equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation
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, Statistics, or related fields No experience required Skills: Strong expertise in the theory and application of birth-death and related stochastic processes Proficiency in both frequentist and Bayesian
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. Demonstrated experience designing analytical frameworks, and experience using machine learning algorithms and Bayesian statistics within the R-language. Demonstrated experience managing project workflows and
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radio interferometry data, particularly very long baseline interferometry. Experience with or skills relevant to statistical modelling and Bayesian inference. Demonstrated familiarity with the fields of X
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of metabolic and cellular properties Phylogenomic analyses of obtained MAGs, including extraction and evaluation of marker genes, performing ML and Bayesian analyses of (concatenated) marker gene sets using
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, multidisciplinary, and international body of participants including hundreds of students, faculty, and practitioners. More information about the General Sessions is available here: https://myumi.ch/EkJbp As a perk of
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expertise to strengthen CTN+ research: · Dr. Shirin Golchi (McGill University) – A biostatistician specializing in adaptive clinical trial design and Bayesian modeling, with experience across multiple
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models, artificial intelligence, Bayesian models, data visualization, dynamic causal models, dynamic systems models, item response theory, large language models, machine learning, mixture models