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noncoding mutations causing diabetes, using advanced statistical genetics, computational regulatory genomics, and analysis of whole genome sequences in unique patient cohorts. Using computational strategies
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, proteomics). The candidate will design and apply integrative data analysis (computational/statistical/causal) and visualization techniques for multi-omic (transcriptomics, mirnomic and proteomics) data
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screens, statistical genetics, and the integration of genetic and regulatory data. This multidisciplinary approach gives us unique opportunities to engage in collaborative projects that combine experimental
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mutational scanning data and for fast and flexible fitting of mechanistic and statistical models to the data. Our current focus is on generating datasets of sufficient size and diversity to train machine
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work with mutant stem cell-derived beta cell organoids, engineered mouse models, single cell epigenomics, large-scale genetic screens, statistical genetics, and the integration of genetic and regulatory