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learning, Bayesian optimization, and real-time sensing with laboratory instrumentation, closed-loop systems can accelerate discovery and improve experimental efficiency. However, building robust and
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Lecturer (BIOS 691) Winter 2026 BIOS 691 Special Topics in Biostatistics (4 credits) Winter 2026 BIOS 691: The aim of this course is to provide researchers with an introduction to practical Bayesian methods
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expertise/interest in Bayesian methods for addressing measurement error. Ideally PhD within the last 5 years. Advanced level experience with R, desired knowledge of Nimble, Overleaf. Excellent communication
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datasets. Proficiency with geometric morphometrics and image alignment. Proficiency in applying quantitative genetic methods to large datasets. Proficiency with large-scale animal models using Bayesian
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-country survey datasets for comparative analysis. Conceptualize and refine a theoretical framework integrating intersectionality and stigma processes. Develop and code a Bayesian meta-regression to pool and
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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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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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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