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Field
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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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anomaly detection Bayesian methods and uncertainty quantification Graph Neural Networks (GNNs) Spatiotemporal data engineering Digital twins and simulation Demonstrated Applied AI for Healthcare and
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Machine Learning Seminar Group Advanced Tutorial Lecture Series on Machine Learning Non-Parametric Bayes Tutorial Course (October 9, 16 and 28, 2008) Bayesian statistics in other labs Machine Learning and
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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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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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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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qualifications: Experience with data assimilation, probabilistic machine learning, Bayesian inference, inverse modeling, and/or simulation-based inference is an advantage. Experience with land-surface models
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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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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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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