60 density-functional-theory-dft Postdoctoral positions at Oak Ridge National Laboratory
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of advanced materials. Research efforts will include the application of density functional theory packages and in-house codes, and the development of supplemental numerical tools, to describe
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Laboratory (ORNL). As part of our research team, you will closely collaborate with a team that includes condensed matter theorists, experts in neutron/X-ray scattering, and experts in thin film and single
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
length/time scales, to provide improved mechanistic insights into nanomaterials response. Bulk of the work will be on novel materials for next-generation microelectronic devices (e.g. oxide ferroelectrics
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carlo), as well as experience in developing and/or applying advanced AI/ML methods to accelerate materials discovery. The project will involve integrating such theory-informed AI-models for creating
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Characterization Section of the Center for Nanophase Materials Sciences (CNMS), Physical Sciences Directorate (PSD) at Oak Ridge National Laboratory (ORNL). As part of our research team, you will investigate
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temperature environments. You will collaborate closely with an interdisciplinary team with expertise in quantum sensing, quantum optics, materials synthesis and processing, and condensed matter theory. This position resides
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strong background in quantum computing, computational physics, and a solid understanding of condensed matter quantum many-body theory. This position resides within the Quantum Computational Science group
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. Related fields, including hydropower and power grid modeling, hydraulic engineering, and sediment transport. The successful candidates must demonstrate an ability to work independently, evidenced by
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develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work with the world's first exascale system
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing