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complex (multicomponent) matrices (foods, digestive products) and the host (cells, animals and humans) in synergy with the expertise available within the department; develop holistic models to simulate and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
analyzing molecular dynamics (MD) simulations to understand water and solute transport through highly crosslinked polyamide systems. The postdoctoral researcher will study polymer chemistry, crosslinking
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Posting Summary Logo Posting Number RTF00169PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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products) and the host (cells, animals and humans) in synergy with the expertise available within the department; develop holistic models to simulate and steer these interactions; combine our expertise
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research. We study the fundamental molecular, cellular, and physiological processes that underly normal and abnormal cardiovascular and metabolic function and we drive the translation of this strong basic
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electron tunneling between the edges of graphene electrodes, with the goal of distinguishing individual molecules, or individual molecular building blocks. The work will involve the following elements
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, and industry partners to gather data and align simulations with practical needs. The postdoctoral researcher will be helping the PI and experts in managing projects, supervising students, funding
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: RNA-polyQ: RNA processing and dysregulation in polyglutamine diseases – insights into pathogenesis and therapeutic targets Principal Investigator: dr hab. Agnieszka Fiszer Department’s website: https
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areas include forest and agricultural plant pathology, plant-soil-microorganism interactions, microbial and molecular ecology, comparative genomics, and biological control. We tackle critical issues
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization