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(URCF) at Drexel University is building a new shared computing platform focused on GPU-accelerated workloads, particularly AI model training. The system includes GPU and CPU compute nodes with Nvidia H200
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-performance computing systems, GPU acceleration, and parallel file systems - Ability to communicate fluently in English, both spoken and written Additional qualifications - Knowledge of or interest in
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Fundación para la Investigación Biomédica del Hospital Gregorio Marañón (FIBHGM) | Spain | 13 days ago
of computing clusters and/or GPUs, as well as basic knowledge of computational fluid dynamics or numerical simulation, will also be valued. The ability to integrate computational tools with real clinical data
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Integration of satellite observations, buoy measurements, and ship-based data into data-driven forecasting systems Demonstration of real-time forecasting workflows running on portable GPU-based edge computing
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) Expertise in further programming languages (in particular C++), GPU programming, parallel programming or high-performance computing are highly valued Keen interest in neuroscience is essential Experience with
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development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D
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Job Code 0005 Employee Class Civil Service Add to My Favorite Jobs Email this Job About the Job The successful applicant will assist in the adaptation of the PPMstar code to run well on GPU-accelerated
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, procurement, and deployment of High-Performance Computing (HPC/GPU) clusters and high-capacity data storage infrastructures in strict compliance with Spanish and EU public procurement regulations. Data
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heterogeneous (CPU/GPU) computing models. Collaborate with physicists, computer scientists, mathematicians and engineers across LBNL divisions to define software requirements, implement robust solutions, and
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. Experience with GPU-based training and high-performance computing. Interest in translating methodological contributions into high-impact medical AI venues (e.g., Nature Medicine, Nature Machine Intelligence