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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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to diverse academic and industrial audiences. Proficiency in Python and deep learning frameworks such as PyTorch. Experience with Linux environments and GPU cluster management is essential. Competent in
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Preferred Qualifications • Experience with GPU programming, shaders, or advanced rendering techniques • Experience integrating external APIs or live data streams • Background in distributed systems or edge
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Fellow of AAPM. Details about Dr. Ren’s profile can be found at the following link: https://www.medschool.umaryland.edu/profiles/Ren-Lei/ Equipment includes a computer cluster with high-end GPUs, and state
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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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scientific software development. Proficiency in C/C++ and Python, with experience in HPC environments (e.g., MPI/OpenMP; GPU experience a plus). Record of peer-reviewed publications appropriate to career stage
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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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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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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 21 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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GPU acceleration, cloud computing, and distributed architectures, to enable efficient analysis of large-scale video datasets. Collaborate with clinical and academic collaborators, external partners