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on developing methods, algorithms, data, and tools, to support autonomous experimentation as well as prediction of industry- and/or community-relevant material properties. key words Machine Learning; Artificial
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resolution techniques are explored to achieve quantitative reconstruction of nanoscale structure images by developing novel DUV/EUV imaging optics and quantitative phase retrieval algorithms. A qualified
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Computational electromagnetics; Integral equations; Numerical algorithms; Fast multipole method; Field solvers; Eligibility citizenship Open to U.S. citizens level Open to Postdoctoral applicants Stipend Base
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water sensor at the molecular level. Our measurement techniques and numerical models based on constrained regularization algorithms allow us to link these measurements with other techniques including
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-acquisition circuitry, and signal-processing/pattern-recognition algorithms. The sensors must be tailored for the particular nature of a given chemical or biochemical measurement problem by optimizing and
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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
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chromatography/mass spectrometry (LC-MS) measurements using principal component analysis, partial least squares, genetic algorithms, and other multivariate statistics. Current projects have accumulated a
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extensive measurement capabilities, CMOS circuit design work for foundry tape-out, and theoretical work developing new algorithms and architectures that leverage the low-energy, high-speed properties
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or algorithms for detection of the drug signal in complex mass spectral data and chemometrics for identification or classification of drug(s) is also of high interest.Through this opportunity, collaboration with
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, adaptable facility that can be used to test advanced control algorithms, generate data for modeling of equipment, test new ideas for grid responsiveness, etc. References Pertzborn, A.J. (2022) ‘Implementation