60 phd-mathematical-modelling-population-modelling 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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. 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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. 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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-time academic or research career. The individual will work primarily on the Duke Predictive Model of Adolescent Mental Health (Duke-PMA) study, a multi-site NIH-funded project that leverages artificial
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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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MD or PhD or equivalent degree and has interests in immunotherapy and/or hematopoietic stem cell transplantation using mouse animal models. The research involves understanding the mechanisms underlying
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University is looking to hire a postdoctoral researcher to contribute to our studies on the molecular and biomechanical mechanisms of cell sheet morphogenesis during dorsal closure in the model system
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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