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Field
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-motivation Experience with at least one of the following areas: excited-state simulation methods, multiscale simulations (QM/MM a plus), catalysis (biological, homogeneous, or heterogeneous), molecular
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-motivation Experience with at least one of the following areas: excited-state simulation methods, multiscale simulations (QM/MM a plus), catalysis (biological, homogeneous, or heterogeneous), molecular
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: A postdoctoral position is available in the Chaudhuri lab within the Nora Eccles Harrison Cardiovascular Research and Training Institute (http://www.cvrti.utah.edu), at the University of Utah
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standard and enhanced sampling molecular dynamics simulations, and machine learning approaches. Participate in manuscript preparation and dissemination of research findings through presentations
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expertise in 1) protein dynamics; 2) molecular dynamics simulations; and 3) high-performance computing environments. Prior programming experience is preferred but not required. The postdoctoral scientist will
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technologies Develop and apply new tools to study mtDNA mutations, copy number, and dynamics Characterize the cellular and mitochondrial consequences of mtDNA mutations using molecular, imaging, and functional
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computer science, computational biology, physics, or related field. Expertise in machine learning or AI. Experience with molecular dynamics simulations and computational workflows. Ability to work in teams
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
knowledge of the molecular dynamics simulations, polymer physical chemistry, and data analysis, Linux operating systems, programming skills and scripting, all of which could be obtained during
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by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description The AICell Lab (https://aicell.io ) in the department of Applied Physics
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on endocrine and neuroendocrine tumours, integrating preclinical models, molecular profiling and translational approaches to identify novel therapeutic vulnerabilities and biomarkers associated with treatment