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Field
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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(SEM), multi-level modelling, Bayesian statistics, or approaches combining quantitative data analyses with machine learning. Documented experience in teaching and related activities, such as
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. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration
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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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, Joshua W. and D.L. Dowe (2005). ``Minimum Message Length and Generalized Bayesian Nets with Asymmetric Languages'', Chapter 11 (pp265-294) in P. Gru:nwald, I. J. Myung and M. A. Pitt (eds.), Advances in
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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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model classifiers (PLS-DA, random forest, neural network, etc) towards unraveling materials structure-function relationships, and are familiar with optimization approaches such as genetic search, Bayesian
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criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building
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data, epidemiologic modeling, Bayesian analysis, modern causal inference, statistical genetics and genomics, machine learning methods, health economics, survey design, systematic reviews, behavioral