65 phd-mathematical-modelling-population-modelling Postdoctoral positions at Duke University
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and health, either with a background in population health analyses or laboratory research experience in molecular toxicology assays based on the zebrafish model. PROJECTED START DATE: The position is
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mathematics and engineering. The Interpretable Machine Learning Lab has dedicated access to high-performance CPU and GPU computing resources provided by Duke University’s Research Computing unit and state
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cells with organoid culture, which will create novel pre-clinical disease models. 3.Identifying vulnerabilities in treatment-resistant epithelial cancers. 4.Developing novel therapies targeting oncogenic
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recent Ph.D. in microbiology, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative
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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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restoration of function. The successful applicant will combine computational modeling, engineering optimization, and in vivo experiments to advance understanding and application of electrical block of neural
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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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tools, high-throughput screening, and human induced pluripotent stem cells (hiPSCs) to model different cell types, phenotypes, and disorders. We are looking for a highly motivated and talented candidate
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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
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system. For the meta-analysis project, Bayesian background with experience in hierarchical modelling and mixed effect models is preferred. The second project, knowledge in survival analysis and machine