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towards improving controllers in turbulent flows. On any given day, you may be called on to: Own and execute novel machine-learning workflows related to wind conditions and control performance. Post-process
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approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer activity and to expand MPRA-based approaches to other aspects
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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
projects using massively parallel reporter assays (MPRA) to quantify the impacts of disease-related genetic variants. Finally, they will interact with nearby collaborators to plan and analyze functional
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requiring initiative and judgment by applying knowledge and understanding of federal and state-level civil procedure to make informed decisions about data categorization. Aggregate and review court filings
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. We develop and apply both experimental and computational approaches, including in vivo Massively Parallel Reporter Assays (MPRAs), to define the sequence basis and functional consequences of enhancer
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
mathematics, or a related field Candidates should have expertise in two or more of the following areas: Uncertainty quantification, numerical solutions of differential equations, and stochastic processes
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will have the opportunity to work with: High‑throughput functional genomics: pooled CRISPR and base‑editing screens, barcoded overexpression libraries, massively parallel reporter assays. Advanced cell
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in AI to study natural and artificial minds in parallel, creating the opportunity to make discoveries about ourselves and to find new ways to understand and improve AI systems. Appointments will be
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD