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. We are interested in candidates with research interests in causal inference or Bayesian methodology, and we also welcome strong applicants from the broader fields of statistics and machine learning
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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. This PhD will focus on uncertainty-aware machine learning models, developing and evaluating techniques (e.g., Bayesian and interval neural networks) to quantify model uncertainty and monitor it during
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Strategy). F3 Experience of interpreting stable isotope data using, for example, statistical modelling approaches such as Bayesian analysis. F4 Experience of ecosystem modelling software, for example Ecopath
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regression analysis to advanced multilevel models and Bayesian analysis to machine learning, among many others. Our two General Sessions , comprising more than forty classes, are offered in a dual-mode format
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scRNASeq and spatial transcriptomics datasets, including probing gene signature expression and comparing expression between groups using correct statistical models (e.g., Linear Mixed Model, Bayesian
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plasma-material interactions in fusion energy systems. You will also advance knowledge of key AI methods such as deep learning, operator learning, and Bayesian optimization, and apply it to develop next
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interests, which include scalable Bayesian methods, spatial statistics, and statistical methods for massive or complex data, in general, and high-dimensional data analysis, statistical machine learning
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mixed models, permutational methods, Bayesian analyses, machine learning algorithms, structural equation modeling). A good practical knowledge of R Personal characteristics To complete a doctoral degree
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designs such as observational study, randomized clinical trial, adaptive randomizations, Bayesian analysis of randomized trials, conventional meta-analysis, meta-regression, and network meta-analysis Work