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Field
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will work closely with the Principal Investigator (PI) on molecular dynamics (MD) or density functional theory (DFT) computations for materials simulation related to battery systems. The candidate may
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conduct research on the computational design of soft materials using molecular simulation and artificial intelligence. The position will support startup-funded research in the Jiang Group focused on
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integration of machine learning into quantum chemical methods and molecular simulation. A particular focus will be on the development, application, and evaluation of modern machine-learning methods
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: Machine-learning interatomic potentials; High-throughput automated workflows for atomistic simulations; Atomistic modeling of the structure of materials; Simulation of molecular diffusion and
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simulations of polymers Desirable Criteria: Molecular modelling and simulation experience during PhD Ability to present complex information effectively to a range of audiences Experience of
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will work with a multidisciplinary team to advance agentic AI tools for simulation, interpretation, data analysis, and scientific discovery. The appointment is expected to last two years and the contract
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of new approaches for physics-based simulation of molecular motions or their application to biomolecular systems of interest and/or the development or application of computational methods related to
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diffraction in plastically deformed alloys. Implement the framework in parallel simulation codes intended to run on LLNL supercomputers. Design and conduct complex molecular and dislocation dynamic
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Synthetic Data Simulation for Malaria Genomic Epidemiology Team: Infectious Disease Epidemiology and Analytics Department of: Global Health Member: Aimée Taylor Scientific Fields Diseases Organisms
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Required: Ph.D. in biomedical engineering, biophysics, molecular biology, immunology, electrical engineering, or a closely related field Strong record of peer-reviewed publications Demonstrated expertise