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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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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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comfortable working in a dynamic environment, and able to demonstrate an ability to think creatively, generate new ideas, and implement solutions. The candidate should be interested in structural
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comfortable working in a dynamic environment, and able to demonstrate an ability to think creatively, generate new ideas, and implement solutions. The candidate should be interested in structural
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comfortable working in a dynamic environment, and able to demonstrate an ability to think creatively, generate new ideas, and implement solutions. The candidate should be interested in structural