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, and algorithm design for inferring conclusions from multiple sources of information. Uncertainty quantification and propagation is vitally important such autonomous workflows, as is the development
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RAP opportunity at National Institute of Standards and Technology NIST Computational Fluid Dynamics & Quantum Algorithms (Multiphase Systems) Location Material Measurement Laboratory, Applied
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and then accessed by a team of experts. We are seeking candidates to address these challenges that range from algorithm development, simulation of reference data, algorithmic accuracy evaluations, design of
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metabolomics. Our studies focus on developing new mass spectral data analysis algorithms (e.g., clustering) to better solve the common key persistent problems arising from factors such as mass shift and peak
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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. Keywords Machine Learning; Artificial
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advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data, with a focus on identifying critical transformation windows and assessing phase evolution kinetics
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, economics, and all branches of science. Current concerns include the development and analysis of algorithms for the solution of problems of estimation, simulation and control of complex systems, and their
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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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that can be integrated into the research workflows used in developing new materials (e.g., carbon nanotubes) or in determining disease pathologies (e.g., Alzheimer’s disease). We want to explore solutions
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-eddy simulation and direct numerical simulation of the phenomena. Topics of interest include algorithm development numerical combustion, scientific visualization, and data analysis. key words Buoyancy