Sort by
Refine Your Search
-
RAP opportunity at National Institute of Standards and Technology NIST Machine learning on facility-scale data and autonomous electron microscopy Location Material Measurement Laboratory, Office
-
catalytic turnover. Integrative modeling and machine learning have the promise of establishing new tools for combining computational and experimental data from HDX-MS and NMR to explain the dynamics and
-
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
-
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
-
: Microbiome; Bacteria; Microbiology; Metabolites; Nuclear Magnetic Resonance, Mass-spectrometry, Chemometrics; Multivariate statistics; Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL
-
sensors for measurements before and during manufacturing processes, analyze the data with a fusion of metrological approaches and machine learning, and monitor and predict the performance of machines and
-
polyethylene and polypropylene. To overcome this challenge, we have previously shown that utilizing machine learning, real-time NIR measurements can be correlated to molecular architecture sensitive properties
-
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
-
advantage of associated particle separations, physical characterization, and chemical analysis. Projects incorporating machine learning and chemometric approaches are also welcome. We are seeking independent
-
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