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software packages) is encouraged to improve reproducibility and to increase usage by the community. [1] https://doi.org/10.1039/C9SM01877H [2] https://doi.org/10.1063/1.5123683 [3] https://doi.org
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mechanism and the charge transport in thin dielectric films deposited on graphene using a non-contact microwave technique ( https://dx.doi.org/10.1021/acs.jpcb.9b11622 ) and monolayer graphene ( https
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Keenan [email protected] 303.497.3665 Karl Francis Stupic [email protected] 303.497.4564 Description Neural net systems have been developed to process magnetic resonance imaging (MRI) data
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analysis, where experiment design is guided by active learning, Bayesian optimization, and similar methods. A key challenge is the integration of prior physics knowledge into the data analysis
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, impairment indicators from other types of drugs are less facile to determine. This is due in part to a dearth of fundamental property data and a lack of measurement infrastructure for difficult to
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the possibility of accelerating materials research and development by orders of magnitude, and it is a core capability and focus area for the Data and AI-Driven Materials Science Group, MMSD, MML
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. The proposal will emphasize the role of metrology, provide findable, accessible, interoperable and reuseable (FAIR) data sets, seek to improve sortation technology for homogeneous recycling
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conductivity. We welcome proposals that would yield thermophysical property data primarily intended for model development that investigate how the molecular size, molecular structure, and polarity of