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interconnection topologies that simplify the use of diverse distributed storage resources through advanced methods for distributed data placement, layout, tiering, and movement. Major Duties and Responsibilities
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Requisition Id 16540 Overview: We are seeking a Postdoctoral Research Associate who will develop and apply computational methods based on electronic structure theory and artificial intelligence
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passionate about leveraging their experience or developing new expertise in composites manufacturing through a variety of methods from benchtop to pilot-scale. They should then be passionate about leveraging
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering
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or related Applied Economics discipline with a strong quantitative focus. Demonstrable knowledge of applied econometrics and statistical evaluation methods, such as: Difference-in-Differences (DiD
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related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
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candidate will join the Multiscale Materials (MsM) group within the Advanced Computing Methods for Physical Sciences Section in CSED. The MsM group is dedicated to delivering multiscale, multi-fidelity
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HPC research within past five years. Preferred Qualifications: The ability to work independently and develop and deploy methods at scale. Experience in high-performance computing and software
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. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of manufacturing processes Understanding
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loads, transmission networks, etc. Develop simulation algorithms that enable large-scale simulations. Integrate (or co-simulate) grid component/device models into open-source software tools for integrated