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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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a spatially explicit predictive model for Everglades vegetation dynamics in response to major drivers. The major objectives are to explore the distribution models that discriminate among prairie and
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candidate will collaborate with investigators within and outside Duke University. The objectives of the projects are: to identify and validate surrogate endpoints of overall survival using data from cancer
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with investigators within and outside Duke University. The objectives of the projects are: to identify and validate surrogate endpoints of overall survival using data from cancer clinical trials in
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mentorship. The goal is to work together with other researchers, students, and staff, to help the applicant and PI mutually achieve their career objectives in a supportive, non-toxic environment. This is a two
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Expertise in quantitative modeling, computational and/or Bayesian methods Expertise using at least one programming languages in the analysis of scientific data such as R, Python, Matlab, or Julia. Expertise