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transcriptases (RT). The project combines structural biology, enzymology, crystallographic fragment screening, and structure-guided drug discovery to define novel mechanisms of RT inhibition and develop new
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reconstruction of events on billion-year timescales, determination of optimal embeddings and encodings for protein structures, multiple structural alignments, relevant artificial intelligence (AI) approaches
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reconstruction of events on billion-year timescales, determination of optimal embeddings and encodings for protein structures, multiple structural alignments, relevant artificial intelligence (AI) approaches
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or OpenShift in production environments, including cluster deployment and configuration, upgrades, scaling, monitoring, troubleshooting, and ensuring overall platform reliability and performance
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light and electron microscopy studies, and microelectron diffraction supporting intramural, academic, and industrial researchers. The RCNF is a pillar of Rutgers’ excellence in structural and cellular
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light and electron microscopy studies, and microelectron diffraction supporting intramural, academic, and industrial researchers. The RCNF is a pillar of Rutgers’ excellence in structural and cellular
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social and structural drivers of health, from a variety of administrative and population data sources. They will contribute to the development of peer-reviewed manuscripts, scientific presentations
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bioinformatics, bioinorganic chemistry, structural biology, microbial genomics, biogeochemistry, molecular evolution, and synthetic organic chemistry that will allow the design of interdisciplinary
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an appropriate psychoeducational evaluation. The administration and scoring of the testing instruments in this course will address issues of reliability and validity, current practices
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computational methods at the density-functional and dynamical mean-field-theory levels, metal-insulator and structural phase transitions, quantum critical points, nonlinear and anomalous responses