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Tenure-Track Biostatistics Faculty - (25002815) Description EPIDEMIOLOGY AND BIOSTATISTICS Tenure-Track Biostatistics Faculty We invite applications for tenure-track faculty in biostatistics to join
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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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). -Interest in Bayesian inference. - Knowledge of non-Gaussian models (heavy-tailed, impulsive) is an asset. Additional Information Work Location(s) Number of offers available1Company/InstituteUniversité
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
Working Title Senior Tenure/Tenure-Track Faculty Appointment Type Tenured/Tenure Track Vacancy ID FAC0005666 Full-time/Part-time Full-Time Permanent Hours per week 40 FTE 1 Position Location North Carolina
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month 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 | about 1 month 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