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Field
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large scale flow network simulations, machine learning, and methods from topological data analysis, to a broad set of problems. Examples include modeling vertebrate and invertebrate circulatory systems
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—specifically fine particulate matter, heat, and humidity—in Philadelphia neighborhoods. The fellow will extend existing research in AI- and data-driven modeling of building systems by developing algorithms and
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and experimental data to bridge our micro- and mesoscopic knowledge of living processes and the intricacies of large-scale networks. By doing so, we aspire to unveil biological principles that can
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statistical techniques that leverage large datasets, heavy computational capabilities, and/or a robust understanding of biological systems to provide unique disease insights. The lab has state-of-the-art
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tumors using high-throughput sequencing technologies. Develop, optimize, and manage bioinformatics pipelines for processing and analyzing large-scale sequencing data (e.g., whole exome sequencing, RNA
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that is production-ready for dissemination to other laboratories and for diagnostic use, and the management of large-scale data. They will join a team of computational and experimental postdoctoral fellows
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that is production-ready for dissemination to other laboratories and for diagnostic use, and the management of large-scale data. They will join a team of computational and experimental postdoctoral fellows
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a pioneer, harnessing state-of-the-art Omics technologies to dissect fundamental biological mechanisms, power large-scale supervised and foundational machine learning initiatives, and comprehensively
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biological processes by developing theory, innovative modeling tools for large-scale biophysical simulations, and computational frameworks for analyzing increasingly large and complex experimental datasets
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be research, data analysis, drafting and publishing research articles, and other scholarly outcomes that arise from a four-year project that uses temporal embeddings of large, heterogeneous scientific