11 molecular-modeling-or-molecular-dynamic-simulation PhD positions at Duke University
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/PhD Degree Experience Background in cardiac biology, neurobiology, bioengineering, or related field Molecular biology Background in electrophysiology preferred but not required Skills Skills in mouse
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available to conduct research on chronic pain. Using animal pain models, behavioral tests, calcium imaging, patch-clamp, optogenetics, neuroanatomy, molecular biology, the successful candidate is expected
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. in microbiology, immunology, systems biology, molecular biology, computational biology, or a related field. • Experience in one or more of the following areas: microbiome research, animal models
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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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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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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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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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maps and model building. You should have a Ph.D. and have more than 2 years of experience in cryo-EM data processing and 3D map generation. You will work under the guidance of Dr. Maria Schumacher on a
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therapeutics will be tested in these models, such as vagus nerve stimulation and anti-inflammatory compounds. Some of these scientific questions will further address the complexity of innate immune signaling
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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