60 phd-in-mathematical-modelling-population Postdoctoral positions at Duke University
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. • Collaborate with mathematical modelers and experimentalists in the NIH Center to iteratively refine learned models. Qualifications: • Ph.D. in applied mathematics, computational science, statistics, machine
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uncertainty quantification into scientific machine learning workflows and optimize the design of computational (ABM) and wet-lab experiments. • Collaborate with mathematical modelers and experimentalists in
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and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated ability to conduct independent research and publish high-quality
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populations across North Carolina and globally, yielding insights into dietary patterns, disease risk, and socioeconomic determinants of health. We are particularly interested in a candidate who could
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northern Kenya. Population Ecology Aging, and Health Network (PECAHN): This postdoc will work with the PECAHN group of research sites, part of the Duke University Population Research Institute, coordinating
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. The Department of Cell Biology is looking for a postdoc candidate to conduct research on tissue morphogenesis using zebrafish as a model system. The candidate will ideally have a training in
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. Experience with field observational studies, handling or large datasets and mathematical modeling are pluses. Other qualifications include a Ph.D. in Biology, Evolution, Ecology or allied fields and evidence
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contributed to the identification of vaccine and therapeutic candidates currently being produced in Good Manufacturing Practice facilities for early phase clinical trials. Minimum Requirements: PhD in
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the nation. The school consists of four departments with 130 tenure-track faculty members, 1250 undergraduate students, 1400 master’s students, and 600 PhD students. Housed within a university renowned for its
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for a Postdoctoral Scholar. The Scholar will conduct research on Bayesian spatiotemporal modeling methodology under the direction of Professor David Dunson at Duke on developing novel models motivated by