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/Statistics Internal Number: JR101137_1774276539 The Data Science and Statistics Program at the University of Richmond invites applications for a two-year postdoctoral research position in the Distant Viewing
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large amounts of geospatial training data to effectively classify grazing, haying, and other grassland management regimes on U.S. agricultural lands. The researcher will also work closely with colleagues
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machine learning approaches with large-scale biological data to automate genome curation by detecting, interpreting, and correcting structural errors, reducing manual effort from weeks to minutes thus
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on research pertinent to the project. By scaling up data, compute and model size, large language models (LLMs) have gained an impressive and ever growing array of capabilities. The next phase of development
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of large-scale biological datasets, applying statistical modelling and computational approaches to high-dimensional data such as bulk and single-cell sequencing, gene expression, proteomics, or metabolomics
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and analyzing large secondary datasets, including but not limited to administrative claims data, electronic health records, national surveys, and disease registries. The applicant should have advanced
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-doctoral associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be
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population-level outcomes. Utilize advanced quantitative methods to analyze large healthcare datasets, including Medicare and Medicaid claims (MedPAR, Outpatient, Carrier, TAF). Develop reproducible code and
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). Remedies and complaint filing deadlines vary by program or incident. Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape
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proteins and pathways implicated on disease pathogenesis. We are currently analyzing brain, CSF and blood, multi-omic data (transcriptomics, proteomics and metabolomics), from a large collection of well