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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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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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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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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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increase throughput and provide rich datasets that can be exploited by machine learning and artificial intelligence. Current advanced mechanical testing activities involve three-dimensional surface digital
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particles. These functional polyplex particles have numerous opportunities for the application of polymers in life science research.[1] There is much to learn concerning their mechanism of formation
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, characterize, and optimize interconnects between disparate chip technologies. Applicants will have the opportunity to learn high-demand skills for millimeter-wave technologies including calibration, integrated