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languages; experience with GPU programming (e.g., CUDA) is highly desirable. Background in optimization, image-guided radiotherapy, medical imaging, or computational modeling. Experience with treatment
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-node GPU training and inference pipelines for foundational models. You'll also develop tools for ingesting, transforming, and integrating large, heterogeneous microscopy image datasets—including writing
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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infrastructure, model training, and inference systems. You'll design, develop, and optimize scalable data pipelines and build multi-node GPU training and inference pipelines for foundational models. You'll also
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. The role involves the design, implementation, and testing of GPU compute kernels, and associated host code, for the CHR real-time pipeline. Particular challenges include high-throughput beamforming via a
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in top-tier machine learning/AI conferences and/or leading scientific journals. Excellent programming skills and hands-on experience with leading machine learning frameworks (e.g., TensorFlow, PyTorch
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between Bonsai RX, animal tracking software, DAQs, and Unity), ROS-like systems, machine vision, GPU programming, shared memory, agent-based modeling, and/or game development. Physical Requirements
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phlebotomy. Additionally, the GPU is home to the GI Division's Motility program offering short and long motility studies, Bravo, Impedance Probes and EndoFlip diagnostic tests. The GPU is also home to
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’ experience post-Ph.D. Expertise in power systems and power electronics. Experience in C/C++, Matlab, and Python programming. Experience in power systems software like PSCAD, PSS/e. Preferred Qualifications
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mathematicians, and domain scientists Develop software that integrates machine learning and numerical techniques targeting heterogeneous architectures (GPUs and accelerators), including DOE leadership-class