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Modeling. Machine Learning Interatomic Potential (MLIP) accelerated simulations. Demonstrated ability of coding in Fortran, Shell, or Python with development experiences. Deep knowledge in excited states and
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for advanced energy storage applications. This position is part of a multidisciplinary program focused on the design, synthesis, processing, and characterization of organic-inorganic hybrid materials capable
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National Laboratory is hiring a Postdoctoral Scholar - AI-Assisted Physical Vapor Deposition of Thin Films within the Molecular Foundry Division. This position will accelerate process development and
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vision for extracting complex patterns, structure, and meaning from images and/or volumes; and (4) new mathematics and algorithms leading to new applications of machine learning and artificial intelligence
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measurements. Experience with thin-film processing, nanofabrication, or device integration. Interest in quantum information science, quantum sensing, or spintronics. Required Application Materials: CV Cover