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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 2 days ago
at Chapel Hill invites applications for a Postdoctoral position in AI driven drug discovery and accelerated molecular simulations. This role will support innovative research at the intersection of Artificial
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operations and chemical processes. Ability to conduct experiments. Experience in using both steady-state and dynamic modeling, simulation and optimization tools, e.g. Aspen, Pro/II, GAMS, gProms... Ambitious
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 hours ago
USNH Employees should apply within Workday through the Jobs Hub app The research position(s) are within the broad area of biophysics, structural modeling, and molecular dynamics (MD) simulations
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molecular tools, capsid architectural modeling, classical nucleation theory, molecular dynamics simulations, and protein expression. The ideal candidate will have a background in mathematical biology
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to apply. We seek candidates with expertise in some or all the following areas: density functional theory, deep learning, high-throughput simulations, molecular dynamics, and materials chemistry. Strong
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correction, or many-body quantum systems. o Proficiency in numerical simulations (e.g., tensor networks, quantum circuit modeling). · For Experimentalists: o PhD in atomic/molecular/optical (AMO) physics
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of HPC Scientific Domain Expertise: While the position is open to various backgrounds, proven expertise in molecular simulation and density functional theory (DFT) is a significant asset to support ongoing
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and nanofabrication. The work also relies on theory and simulation, specifically focusing on numerical mean-field electrostatics calculations and Brownian Dynamics simulations. The group is looking
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materials using statistical mechanics, molecular simulations, and machine learning. Expectations Candidates will be responsible for: Developing multi-scale modeling methods for polymeric materials, using