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Ravel [email protected] 631.344.3613 Description Develop methods of applying machine learning and artificial intelligence to synchrotron experimentation. This opportunity will be focused on operations
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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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with strain engineered into the channel to improve carrier mobility while residual strains from manufacturing processes can lead to mechanical failures. Similarly, quantum computing architectures use
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has an active effort in the development of electron microscopy methods for high spatial resolution materials characterization and has recently upgraded its aberration-corrected STEM with a high-speed
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://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
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performance modeling capabilities that simultaneously consider multiple performance aspects, robust IAQ and other performance metrics, and measurement methods, sensors, and data to evaluate and verify building
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chemicals. While neutron and X-ray scattering methods are workhorse techniques for characterizing model formulations, the large number of components in many real products makes mapping the high-dimensional
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processing of tables, extraction of property data from plots, analysis of paper content and extraction of metadata (substances, description of their samples, experimental methods, uncertainties, etc
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of the instrument fail or are attacked by an adversary. We would like to address the safety concerns by researching a metrology for establishing digital references [2], safety zones (boundaries), validation methods
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informatics developers in the public-private-academic Genome in a Bottle Consortium to develop methods to integrate short-, linked-, and long-read sequencing technologies to form benchmarks for somatic variant