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project . Fluent oral and written communication skills in English Background in biomarker analysis and/or compound specific isotope analysis and/or archaeometric dating techniques and Bayesian statistics
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datasets. Statistics and mathematics Strong grounding in multivariate statistics, dimensionality reduction, and latent variable modeling. Experience with temporal or dynamical modeling, Bayesian inference
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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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modelling analyses, including differential gene expression analysis, microbiome diversity analyses, host–microbiome association testing, metagenome-wide association analyses (mGWAS), hierarchical Bayesian
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Elhoseiny, Code: https://github.com/yli1/CLCL Uncertainty-guided Continual Learning with Bayesian Neural Networks (ICLR’20), Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell, Marcus Rohrbach, Code: https
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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