63 phd-mathematical-modelling-population-modelling Postdoctoral positions at Duke University
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research topics include mathematical modeling of microtubule dynamics in neuronal cells, analysis and simulation of RNA transport and organization in developing cells, and parameter inference and
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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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 (b) the critical role of structural and functional connectivity using combined tractography and graph theory analyses. Our modeling of mnemonic representations uses the latest tools available to AI
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cultured cells and animal models of skin diseases. Work Performed • Development of new and implementation and modification of existing experimental procedures. • Data preparation for oral presentations
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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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. Candidates with non-US degrees may be required to provide proof of degree equivalency. Preferred Qualifications: A PhD or MD/PhD (or equivalent) in biological sciences (cell & developmental biology or a
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will lead methodological innovation and applied research in predictive modeling for mental health, drawing on diverse data modalities such as: Electronic Health Records (EHR) Patient-reported outcomes
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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