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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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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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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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conducting Illumina sequencing runs, as well as data analysis, is highly preferred. Experience in biochemistry and structural analysis of RNA -processing enzymes is desirable. Equipment Utilized Must
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. Coordinates and oversees data management activities, including development and implementation of data quality assurance and control procedures to ensure integrity and reliability of study data
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human insulin and IGF‑1. We investigate how VILPs interact with and inhibit the human IGF‑1 receptor (IGF1R) with high specificity, without affecting the insulin receptor. Using structural biology (cryoEM