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materials. The primary focus of this work is on mechanical characterization, microstructural analysis, and finite element analysis (FEA) and artificial intelligence (AI)/machine learning (ML) modeling
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and distributed control intelligence that can be applied to solve these problems through the application of machine learning, intelligent optimization techniques, automated fault detections and
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functionalities, from crossbar-based machine learning to race-logic-based computing. Opportunities exist for experimental work in device fabrication and measurement using NIST’s state-of-the-art NanoFab and
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consideration will be made to candidates with experience in automation or machine learning. The postdoc will join a group which is focused on pioneering applications of modern machine learning methods, FAIR data
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and Techniques 59(1): 188, 2011 Database; Microelectronics; Machine learning; Data informatics; Physics; Terahertz; Metrology;
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manufacturing processes, analyze the data with a fusion of metrological approaches and machine learning, and monitor and predict the performance of machines and their processes. Semiconductor manufacturers desire
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; Microelectronics; Machine learning; Data informatics; Physics; Terahertz; Metrology; Chemistry; Materials engineering;
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properties, as well as the absence of commonly accepted machine-readable data formats, requires tremendous human efforts to acquire the desired information from scientific papers, analyze the quality and
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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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301.975.3507 Description Recent developments in Artificial Intelligence (AI) have allowed machine learning models to solve certain complex problems in natural language processing and other areas at large scales