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Laboratory. doi: https://doi.org/10.6028/NIST.TN.2178 . building control; intelligent agents; optimization; data analytics; machine learning; data-driven models; HVAC
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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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experience preferable). Staff webpage: https://www.nist.gov/people/david-deisenroth Testbed wepage: https://www.nist.gov/el/intelligent-systems-division-73500/flami key words metal additive manufacturing
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: Microbiome; Bacteria; Microbiology; Metabolites; Nuclear Magnetic Resonance, Mass-spectrometry, Chemometrics; Multivariate statistics; Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL
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-throughput characterization methods include spectroscopy, optical and scanned probe microscopy, scattering, reflectivity, ellipsometry, and contact angle measurements. See http://www.nist.gov/mml/polymers
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RAP opportunity at National Institute of Standards and Technology NIST Coupling electronic structure methods, artificial intelligence, and data-driven approaches for next-generation quantum
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testing novel MOF materials for applications in carbon capture (https://doi.org/10.1016/j.xcrp.2022.101063). Successful candidates must have a background in MOF synthesis and characterization. Special
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multiple 2D images and multiple channels, (d) optimizing 2D projection viewpoints (dose reduction and time savings), (e) applying artificial intelligence and traditional machine learning models to noise
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[2017]. https://www.nature.com/articles/s41598-017-12063-6.pdf [4] NIST News, “NIST’s quick test may speed antibiotic treatment and combat drug resistance,” September 2017. https://www.nist.gov/news