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. Nanotechnology (26)44: 444002, 2015 Mueller T, Kusne AG, Ramprasad R: Machine Learning in Materials Science: Recent Progress and Emerging Applications. Reviews in Computational Chemistry, June 2015 Materials
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at NIST and external collaborators, a successful candidate will extend our recently developed ML-driven autonomous systems for state assessment, calibration, and control of quantum information science
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RAP opportunity at National Institute of Standards and Technology NIST Identifying Material Behavior from Measurements and Simulations in Advanced Mechanical Testing Location Material
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RAP opportunity at National Institute of Standards and Technology NIST Enabling Advanced Functionalities in Photonics using Low-Dimensional Semiconductors Location Material Measurement
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RAP opportunity at National Institute of Standards and Technology NIST High Frequency Electrical Metrology for Three-Dimensional Integrated Circuits Location Physical Measurement Laboratory
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RAP opportunity at National Institute of Standards and Technology NIST Decay Energy Spectrometry (DES) Using Transition Edge Sensors (TES) For Measuring Absolute Activity of Radionuclides
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RAP opportunity at National Institute of Standards and Technology NIST Immersive Visualization Location Information Technology Laboratory, Applied and Computational Mathematics Division
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RAP opportunity at National Institute of Standards and Technology NIST Applications of Machine Learning/AI to Neutron Scattering Location NIST Center for Neutron Research opportunity location
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access to. Qualified candidates will have a background in electron microscopy or a relevant branch of computer science. key words Scanning transmission electron microscopy; Nanocharacterization; Electron
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RAP opportunity at National Institute of Standards and Technology NIST Finite Element and Crystal Plasticity Modeling for the Development of Lightweighting Materials Location Material