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well as large-scale GPU computing facilities for deep learning. Our Lab aims to hire a Research Fellow to lead a research project on Real-World Deepfake Detection and Image Forgery Localization. The role will
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part of the core PLI team, which includes top-tier faculty, research fellows, scientists, software engineers, postdocs, and graduate students. Fellows will have access to the AI Lab GPU cluster (300
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, molecular biology, and psychology. The work will leverage GPU computing on the LUMI supercomputer, one of Europe’s fastest, to perform state-of-the-art molecular simulations. The position includes secondments
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research in numerical relativity, computational general relativity, or a closely related area of computational physics. Experience with PDE solvers (elliptic and/or hyperbolic), numerical methods, and
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made at the Postdoctoral Research Associate rank. The AI Postdoctoral Research Fellow will have access to the AI Lab GPU cluster (300 H100s). Candidates should have recently received or be about to
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GPU acceleration, cloud computing, and distributed architectures, to enable efficient analysis of large-scale video datasets. Collaborate with clinical and academic collaborators, external partners
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 8 hours ago
approaches for automated medical devices (e.g., physiologic closed-loop controlled devices). Developing multi-spectral computational modeling tools using GPU-based processors to map light propagation
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samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize, and validate a refractive
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of thick and strongly scattering samples. Optimize reconstruction algorithms for efficient large-scale 3D imaging, including high-performance and GPU-accelerated computing where appropriate. Design, optimize
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environment spanning computational chemistry, cell biology, physics, and materials science. The work will leverage GPU computing on high-performance supercomputers such as Saga and LUMI to accelerate drug