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theory, stochastic dynamical systems, hierarchical Bayesian models, optimization, mean field games, and applications of these methods to renewable energy related topics such as atmospheric modeling, wind
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series, and Bayesian analysis. In applied mathematics, these include information theory, coding theory, control theory, fluid mechanics, and mathematical biology. Further details on the departmental
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opportunities for collaboration and postdoctoral support. The Department is very active in research areas including Bayesian statistics, biostatistics, mathematical statistics, optimal design of experiments
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foundation in data synthesis, remote sensing, spatial analysis and Bayesian statistics o Strong proficiencies in R and/or Python o Experience with version control in GitHub o Experience conducting
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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): applied optimization, Bayesian inference, big data analysis (especially as applied within astronomy or medical physics), computational statistics, data visualization, deep learning or statistical learning
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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred