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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 22 days ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 16 days ago
of Biostatistics. Specifically, the position works on and provides oversight to several federal and industry research and training grants in the areas of casual inference, Bayesian methods, robust methods, frailty
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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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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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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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Date 10/16/2025 Requisition Number PRN43336B Job Title Biostatistician I Working Title Biostatistician I Career Progression Track E Track Level FLSA Code Professional Patient Sensitive Job Code? No
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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