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Field
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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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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Job Posting Title: Lead R&D Software Engineer, Software/Hardware Integration ---- Hiring Department: Applied Research Laboratories ---- Position Open To: All Applicants ---- Weekly Scheduled Hours
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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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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures
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the different types of systems and develop a core graph data system that can serve as a common building block. This way, redundancies in keeping multiple cop-ies of graph data in different systems could be
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models at multiple scales are usually developed and combined into a single multi-scale/multi-physics model. With this process, analysis and optimization are possible. Computational analysis and design of
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the development of image/signal processing algorithms from a multidisciplinary approach, to include multiple sensor modalities. These multidisciplinary research opportunities incorporate theoretical and