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Field
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program AlphaCell is a new SciLifeLab initiative aimed at building the first molecular-level computational model of the human cell. The program is led by Jan Ellenberg (Director of SciLifeLab) and Mathias
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well as with theoretical researchers specializing in atomistic simulation, density functional theory (DFT), and ab initio molecular dynamics (AIMD). The successful candidate will also engage with collaborators
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, or foundation models. Familiarity with atomistic simulations (e.g., density functional theory, molecular dynamics). Interest in developing broadly applicable machine-learning methods for physical sciences
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the intersection of plant pathology, microbiology, molecular biology, and computational biology. The research focuses on understanding the population dynamics of fungal communities throughout the growing season and
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candidate must have extensive experience modeling the proteins of cardiac muscle and with such rare event simulation methods as Transition Path Sampling and Metadynamics. The Department of Chemistry and
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a Postdoctoral Research Associate to train in interdisciplinary projects involving developing new AI-driven Molecular Dynamics (MD) simulation methods and apply them to drug discovery in multiple
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datasets into scalable, standardized, and AI-ready frameworks for computational modelling. Working in SciLifeLab’s highly interdisciplinary environment, where imaging, molecular data, and computational
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models, behavioral pharmacology, molecular biology, in vivo neurochemical assessments, and physiological measurements. Essential Function Yes Percentage of Time 40 Job Duty Data analysis: Responsible
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 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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, electrocatalysis, and/or battery technologies. Experience in molecular dynamics simulations (including AIMD). Experience with machine leaning interatomic potentials. Strong programming experience with PyTorch