349 algorithm-development-"Prof"-"Washington-University-in-St"-"Prof" positions at NIST
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Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
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function; (2) explore available algorithms for image post-processing to recover original information about the visual environment; (3) explore scientific applications of new imaging methods; and (4) develop
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Description The goals of this project are to develop new assays to support the production of safe and effective protein therapeutics. The new generations of biopharmaceuticals are produced using cells in
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and signal-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
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-deconvolution algorithms that can account for peak asymmetry due to imperfect shims; the use of spatially selective or multidimensional NMR methods; and the development of reference materials, especially for gas
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NIST only participates in the February and August reviews. Project Description:NIST is developing a novel neutron interferometric phase imaging method using a grating-based, far-field interferometer
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modalities such as MRI, computed tomography (CT), positron emission tomography (PET), and ultrasound (US). These combined techniques such as PET-MR or MR-US have unique challenges when developing quantitative
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to develop traceable materials and methods to characterize the performance of magnetic resonance imaging (MRI) scanners, particularly their ability to noninvasively measure diffusion properties, such as the
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course-grained simulations of nanotubes with large adsorbed dispersant molecules in solution. It is expected that the challenging nature of these simulations will require the development of novel
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these complex communities by developing sample preparation techniques that are compatible with NMR and mass spectrometry-based techniques. This will allow parallel multimodal analysis including proteomic