62 phd-mathematical-modelling-population-modelling Postdoctoral positions at Duke University
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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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cultured human and rodent cardiomyocytes, engineered heart tissues, and animal models of heart development and disease. Specifically, you will engage in basic science and applied research to explore
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website. TERM One year, with the possibility of another year extension depending on successful progress. QUALIFICATIONS Applicants should hold a PhD in Earth Science, Environmental Science, or a related
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pathways and mechanisms underlying autoimmunity from a lncRNA and epigenetic gene regulation perspective. We utilize biochemical assays, tissue culture, mouse transplantation & disease modeling experiments
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basic research and animal models, preferably rodents. The ideal candidate will have a strong background and experience in the field of neuroscience and behavior in rodent models, such as social behavior
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of the intestines induced by radiation, with a focus on mechanisms of regenerative reprogramming. Complementary approaches will be used, including genetically engineered mouse models, histopathology, mouse and human
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keratinocyte differentiation and cancer biology utilizing cultured cells and animal models of skin diseases. Responsibilities include: Development of new and implementation and modification of existing
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-throughput genomic screening approaches, primary cell culture systems, and animal models of influenza disease. For more information visit https://mgm.duke.edu/faculty-and-research/primary-faculty/nicholas
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-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine learning is desired
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by two NIDA-funded grants. The first project, NEURONIC, utilizes a nasal spray paradigm to assess reactions to nicotine in a controlled, laboratory setting as a model of risk for addiction