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) calculations to significantly improve the state-of-the-art theoretical understanding of electrostatic environments in liquid-phase, at solid-liquid interfaces, and in nanoscale confinement. Qualified candidates
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the synthesis-process-structure-property relationship for quantum solid state materials. References: Liang, et al., 2025. Real-time experiment-theory closed-loop interaction for autonomous materials science
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part of a research recommendation engine to guide researchers in the lab. The algorithms integrate machine learning, solid state physics, on-the-fly physical experiments, on-the-fly simulations
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alloys, carbon-based composites, and solid-state-biomolecule hybrid structures. Our data-driven development uses cheminformatics methodologies combined with machine learning methods to produce predictive
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ions that are confined in electromagnetic traps and laser-cooled, in some cases to the ground state of motion. Experiments employ RF (Paul) traps and Penning traps. Precision spectroscopy experiments
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RAP opportunity at National Institute of Standards and Technology NIST Fundamental Physics with Cold Neutrons Location Physical Measurement Laboratory, Radiation Physics Division opportunity