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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials
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. The ideal candidate will enhance our biostatistical core and complement or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials
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or deepen our current department strengths, including, but not limited to: Bayesian methods, big data, causal inference, clinical trials, machine learning, mobile health data, real world evidence, survival
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and applied mathematics, statistics, and math education. We are seeking a tenure-track Assistant Professor to join our statistics group and contribute to its growth and success. We invite applications
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version control and containerization (Docker/Singularity) Statistical Modeling: Quantitative data analysis using GLMs, Bayesian methods, or mixed-effect models to interpret complex perturbation datasets
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, surveys, experiments, simulations, Bayesian inference, and advanced quantitative analysis. We are especially interested in courses on the applied use of generative AI, including courses on developing and
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the graduate curriculum, and social sciences scholarship across the school. Examples of topic areas include (but are NOT limited to): models for inference (e.g., SEM/CFA, Bayesian modeling, linear mixed effects
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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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tenured/tenure-track faculty and nine full-time instructors. Current research areas of the faculty include survival and reliability analysis, Bayesian statistics, latent variable methods, item response
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, and social sciences scholarship across the school. Examples of topic areas include (but are NOT limited to): models for inference (e.g., SEM/CFA, Bayesian modeling, linear mixed effects), data mining