83 data-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"U.S" positions at NIST
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catalytic turnover. Integrative modeling and machine learning have the promise of establishing new tools for combining computational and experimental data from HDX-MS and NMR to explain the dynamics and
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designs. In addition to fabrication and characterization of these measurement tools, we also develop new readout schemes, signal and data processing, control systems, and biomimetic surfaces to improve
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information over quantum networks but can also be used for high-efficiency optical modulation and microwave sensing applications. Most devices of interest here will convert microwave photons to acoustic waves
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variables. Computer-controlled equipment is available for alternating-current magnetic-susceptibility measurements as a function of frequency, temperature, and magnetic field. An automated vibrating sample
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capabilities for characterizing nanoparticles at the single-particle level. There will also be opportunities for interested candidates to develop advanced data processing techniques. microscopy; light scattering
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analysis at lower frequencies, we can obtain frequency-dependent impedance data over the extremely broad frequency range from several 100 Hz to 100 GHz. The electrical impedance of planar measurement
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of small volumes of liquid samples as a function of temperature, composition, and concentration. These measurements can yield information on the polarization dynamics of inorganic nanoparticles, organic
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system, offers higher sensitivity to surface displacement (noise floor of ~ 0.1 pm) than quasi-static PFM (~ 10 pm). However, conversion of the experimental results to quantitative displacement data is
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differentiation, data describing the changes in gene expression at the single cell level are needed. In this project, quantitative live cell imaging and image analysis will be used to follow gene expression
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systems. This work will specifically focus on combining ML algorithms with classical data analysis and control techniques to develop robust in situ (i.e., in real-time, during the operating experiment