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development and is the nexus of a broad collaboration network. Each year, CFN staff members support the research of nearly 600 external facility users. Three strategic nanoscience themes underlie the CFN
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the development, analysis and assessment of policies that strengthen international regimes, collecting and analyzing data to identify trends and threats to the U.S. and international communities, developing and
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for the characterization. Prepare research based manuscripts for publication Participate in the meetings related to the EFRC Required Knowledge, Skills, and Abilities: PhD in Condensed Matter Physics, Materials Science
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. The program involves close collaborations with experts in theory and data science and will benefit from frequent interactions with principal investigators at the National Synchrotron Light Source II (NSLS-II
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experimental modalities will be developed to probe the non-equilibrium properties of rare-earth based magnetic chain systems. These modalities will exploit the unusually long lifetimes present in such materials
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push the envelope to accomplish our missions, we develop novel tools and techniques in biochemistry, molecular genetics, structural biology, cell biology, plant pathology and bioimaging that also benefit
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of biological systems from how plants make oils and other products to the role of proteins in disease. Our work helps to develop and make use of the tools and techniques of biochemistry, molecular genetics
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Foundation for the State University of New York on behalf of Stony Brook University. ORGANIZATION OVERVIEW The mission of the Instrumentation Division is to develop state-of-the-art instrumentation required
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scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) novel development of deep learning ML models and adaptation of existing ones
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on diverse scientific and security problems of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and adaptation