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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Computational Fluid Dynamics & Quantum Algorithms (Multiphase Systems) Location Material
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STFP Home sign in | focus RAP opportunity at National Institute of Standards and Technology NIST Algorithms for Compound Identification by Mass Spectrometry Location Material Measurement
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@nist.gov 301.975.6974 Meltem Sonmez Turan [email protected] 301.975.4391 Description NIST standardized cryptographic algorithms are intended to be "bulletproof". That is, the computational
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Aaron Gilad Kusne [email protected] 301.975.6256 Description We are developing machine learning algorithms to accelerate the discovery and optimization of advanced materials. These new algorithms form
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algorithms for advanced parallel and distributed computational platforms, prevent scientists from advancing in their domains. The challenges include the facts that (1) microscopy image acquisition generates
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functionality into microbial systems. The postdoc will develop machine learning algorithms to analyze phenotype and sequence data, as well as active learning algorithms to optimize and control experiments in
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pipelines to process the vast, high-speed XRD datasets generated during AM processes. These pipelines will utilize advanced machine learning models and physics-informed algorithms for analyzing high
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(e.g., Alzheimer’s disease). We want to explore solutions that combine parallel architectures (i.e., GPUs or accelerator boards, clusters) and numerical algorithms suited to such architectures with the
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to solve important problems in engineering, economics, and all branches of science. Current concerns include the development and analysis of algorithms for the solution of problems of estimation
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-driven autonomous systems for state assessment, calibration, and control of quantum information science systems. This work will specifically focus on combining ML algorithms with classical data analysis