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RAP opportunity at National Institute of Standards and Technology NIST Autonomous experimentation and machine learning of material properties Location Material Measurement Laboratory, Materials
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of materials science and engineering. The successful applicant would work with a team of experts including experimental materials scientists, computational materials scientists, machine learning experts
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mirrors based on Wolter optics, development of denoising and segmentation algorithms using machine learning [3], neutron energy-selective imaging, neutron phase grating imaging, polarized neutron imaging
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RAP opportunity at National Institute of Standards and Technology NIST Combining Theory, Simulation, Machine Learning, and Autonomous Experiments for Industrial Formulation Discovery Location
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technology. Reference Lee CH, et al: Exploiting dimensionality and defect mitigation to create tunable microwave dielectrics. Nature 502: 532-536, 2013 key words Electronics; Microelectronics; Machine learning
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are interested in using Machine Learning and AI techniques to enable autonomous, AI-Driven, experimental research. There are many aspects of this nascent field that require further development. This includes
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. By leveraging material simulation and Machine Learning Interatomic Potentials (MLIPs), we aim to accelerate the interpretation of inelastic (INS) and quasi-elastic neutron scattering (QENS) data
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RAP opportunity at National Institute of Standards and Technology NIST Materials Discovery Using Synchrotron Radiation, Machine Learning, and Artifical Intelligence Location Material Measurement
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; Microelectronics; Machine learning; Data informatics; Physics; Terahertz; Metrology; Chemistry; Materials engineering;
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RAP opportunity at National Institute of Standards and Technology NIST Machine Learning Driven Autonomous Metrology System Location Physical Measurement Laboratory, Sensor Science Division