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XRD measurements mostly at NIST’s Beamline for Materials Measurements, collaborating with scientists from NIST Gaithersburg, and developing work-flow and data-analysis tools for managing and
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RAP opportunity at National Institute of Standards and Technology NIST Scientific machine learning methods for trustable accelerated materials characterization and design Location Material
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Information Technology Laboratory, Applied and Computational Mathematics Division NIST only participates in the February and August reviews. Machine Learning (ML) and artificial intelligence (AI
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collaboration with NIST and outside experimentalists. Successful candidates will have experience in density functional theory, machine learning and/or quantum techniques with some exposure to atomistic modeling
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, including research in deep learning for genomics and “explainable AI”, and collaborating with Genome in a Bottle Consortium members and others from companies, academia, and government. [1] JM Zook, et al
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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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RAP opportunity at National Institute of Standards and Technology NIST Machine Learning for Autonomous Genetic Engineering of Microbial Systems Location Material Measurement Laboratory
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measurement assurance to support the control and rational design of biological function. Through state of the art synthetic biology, automation, and machine learning, the Group creates living measurement
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learning, the Group creates living measurement systems, such as cells, engineered to sense and respond in programmed ways. Importantly, building these systems both requires and advances meaningful
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are essential for broad adoption of these methods, this postdoc would collaborate with a unique array of technology and informatics developers in the Genome in a Bottle Consortium to develop authoritative de novo