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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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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 4 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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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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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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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
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modern high performance computation facilities and parallel computing clusters (CPU and GPU). Excellent publication record and demonstrated conference presentation skills. Demonstrated ability to operate
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multiphase flows Your tasks Develop and extend the in-house GPU-accelerated multiphase Lattice Boltzmann (LBM) code for DNS-grade boiling multiphase flow related to nuclear reactor operation, including bubble
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required) Experience with machine learning / deep learning (PyTorch; model training; GPU workflows). Experience with Transformers / text embeddings / multimodal modeling (e.g., Hugging Face ecosystem