15 molecular-modeling-or-molecular-dynamic-simulation PhD positions at Duke University
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(or equivalent) in biological sciences. Strong research background in cell biology, molecular biology, mouse models of cancer, and/or biochemistry. Prior experience in stem cells, vascular biology, 3D organoid
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Postdoctoral position at Duke University is available to conduct research on chronic pain. Using animal pain models, behavioral tests, calcium imaging, patch-clamp, optogenetics, neuroanatomy, molecular biology
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. POSTDOCTORAL ASSOCIATE Drs. Deborah Muoio and Paul Grimsrud invite applications for a postdoctoral fellowship position at the Duke Molecular Physiology Institute (DMPI) within the School of Medicine at Duke
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of immunology and molecular biology preferred. Candidates should have excellent scientific writing and strong oral communications skills Experience in leading research papers for publication and data
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phenotyping in preclinical models, flow cytometry, confocal and light sheet microscopy, and many other standard molecular/biochemical techniques. Principal Responsibilities: • The candidate will work under the
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biology, or immunology. Work in our lab involves computational modeling, molecular biology, protein engineering, recombinant protein expression and purification, high-throughput library screening, tissue
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position is funded by multiple NIH projects, e.g., https://tinyurl.co m/ysxhmujvThe overall goal is to : (1) develop inference and dynamic prediction models using a wide variety of data, including clinical
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effects on cognitive outcomes post-surgery. This project is part of a dynamic, cross-disciplinary collaboration involving both the Anesthesiology and Physics Departments at Duke University. Key
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physiology, behavioral analysis, genetic models, chemistry and toxicology to examine the effects of flavor additives in electronic cigarettes and other tobacco products on nicotine use initiation and health
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key regulators of inflammation and tissue remodeling in gut and skin diseases. • Apply and refine AI/ML methods, including deep learning, neural networks, and interpretable models (e.g., SHAP, BioMapAI