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Bayesian inference, probabilistic modeling, and machine learning, the project aims to make Arctic observations more efficient, intelligent, and impactful. You will integrate field observations—including
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The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is
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processes of the study systems of our collaborators. Core components of the research involve, among others, Bayesian hierarchical modelling, shrinkage methods, machine learning (ML) or dimension reduction
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modelling analyses, including differential gene expression analysis, microbiome diversity analyses, host–microbiome association testing, metagenome-wide association analyses (mGWAS), hierarchical Bayesian