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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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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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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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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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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
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, and site maps to guide development. Utilizes Drupal, Illustrator, and other design and wireframing tools for visual and structural planning. Implements HTML , CSS , and JavaScript for rapid
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analyzing neuroimage data, including structural CT/ MRI , resting state fMRT, DTI , and task-based fMRJ. The neuroimaging data will be interpreted along with the study subject’s demographic information and