290 computational-physics-"https:"-"https:"-"https:"-"https:"-"Caltech" positions at NIST
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, (2) interpretation of experimental spectra, (3) development of semi-empirical methods, (4) studies of reactivity indices, (5) computational electrochemistry, and (6) chemical informatics. The explosion
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-ray detector developed at NIST offers abundant opportunities to conduct new experiments in chemistry and physics. Its high energy resolution should allow us to observe changes in oxidation state as a
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be inferred from initial or final state materials property measurements such as sorbent microstructure, but must be measured in situ during the sorption or release process. This project focuses
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structure-property relationships for polymers has been largely limited due to the inability to systematically control polymer sequence especially under real-world conditions where process history
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quantum computing. Their dimensions range from a few to several hundred nanometers. There is special interest in color centers in nanodiamonds, which give them unique photonic and spin characteristics
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increasingly clear that Machine Learning/AI are having great impacts across a number of fields of physics. This research opportunity revolves around applying these techniques towards optimizing experimental
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, or techniques that will speed up analysis times, provide increased information to the chemist, and/or simplify data interpretation while enhancing data quality. One of the goals of the forensic program at NIST is
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knowledge and experience with material science, chemistry or physics. References Lin Y; Okoro CA.; Ahn JJ; et al: Broadband Microwave-Based Metrology Platform Development: Demonstration of In-Situ Failure
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for critical applications that require qualification and certification—increasingly require that computational models and in-situ monitoring of such processes be experimentally validated under highly controlled
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the existence of underlying physics. This project seeks to incorporate physical laws and domain knowledge into machine learning to improve performance with regards to small datasets, extrapolation and