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Old Dominion University Research Fountation | Norfolk, Virginia | United States | about 11 hours ago
collocation schemes for simulation of 3-D compressible viscous flows on unstructured grids. Our department has a history of producing exceptionally successful, independent, and productive postdocs and PhD
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, and lab inventories related to cellular and molecular biology techniques that will include common use items (e.g., compressed gas, dry ice and liquid nitrogen). Guide the work of others and/or provides
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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data
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sequence programming (e.g., IDEA/ICE) and contemporary image reconstruction techniques (e.g., compressed sensing, parallel imaging, model-based or deep learning reconstructions). Knowledge of radial data
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expectations. Preferred Qualifications Previous experience with turbulence over rough walls, porous media, or complex geometries, as evidenced by work history. Knowledge of compressible flow regimes, including
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, Merced is looking for an ambitious Postdoctoral Scholar to carry out a project that leverages compressed sensing to accelerate proteomics research. The position is NSF-funded, and the scholar will work
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of compressible flow regimes, including supersonic and hypersonic flows, as demonstrated by application materials. Familiarity with machine learning or data-driven modeling approaches in fluid dynamics, as
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, biologists, and data scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and
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processing, prepreg systems, resin transfer molding (RTM), vacuum-assisted resin transfer molding (VARTM), and other infusion or compression molding techniques. 2. Advanced Carbon-Carbon Composites for Extreme
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scientists. The emphasis will be on enabling high-fidelity image reconstructions from sparse and noisy data, leveraging state-of-the-art methods in compressed sensing, optimization, and machine learning