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project aims to unravel the molecular mechanisms underlying these processes in lactating mammary epithelial cells. Using a cutting-edge lactation organoid model developed in our laboratory, combined with in
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project involves interdisciplinary research at the interface of computer science and mathematics, with a focus on bivariate molecular machine learning for modeling molecular interactions and properties
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the mechanical behavior of these materials at the nanoscale. Subsequently, a molecular dynamics model will be developed to simulate the matrix–nanotube interaction, analyzing the effects of adhesion, orientation
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, or a closely related discipline with a strong academic record Genuine interest in data-driven and physics-based modeling, molecular simulations, and their application to bioprocesses and bioseparations
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link: https://www.ncbi.nlm.nih.gov/myncbi/nathan.lawson.1/bibliography/public/ Send related inquiries to: Nathan D. Lawson, Ph.D. Professor Department of Molecular, Cell, and Cancer Biology University
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investigate HIV-induced vascular dysfunction and neuroAIDS molecular mechanisms using in vitro and in vivo (animal models) approaches. Specifically, the person will utilize HIV mouse models and HIV-1 infected
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19 Jan 2026 Job Information Organisation/Company Institute of Molecular Genetics of the Czech Academy of Sciences Research Field Biological sciences Medical sciences Researcher Profile First Stage
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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Postdoctoral researcher (M/F), synthesis of crystal phase heterostructures by Molecular Beam Epitaxy
optimize the growth of GaAs nanowires integrating crystal-phase heterostructures by molecular beam epitaxy (MBE). Contribute to scientific writing, presentation of results, and promotion of the project's
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by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Join us to pioneer next-generation generative models that accelerate molecular dynamics. We