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modules, and monitor training progress. Display performance metrics (e.g., inference time, GPU utilization, throughput, ROI impact) in real time. System Integration Work with the research team to connect AI
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people and cultures for the betterment of humanity. KAUST offers superb research facilities, including the Shaheen supercomputer (listed as top 20 in the HPC world list) sporting 3000+ GPUs; generous
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scientists, software and AI engineers, and neuroscientists, you’ll have access to high-performance workstations, CPU/GPU clusters, and experimental systems tailored for fish and fly research. This role will
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/section members. Access to data sets and the GPU super-computing environment is readily available for immediate use. Essential experience and skills PhD in Computer Science, Computational Biology
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and optimization strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data
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conducted in collaboration with NVIDIA, leveraging state-of-the-art GPU-based simulation environments and AI platforms. The position will be hosted within the Medical Imaging and Robotics group led by Dr
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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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familiarised with the hydrodynamics of circumstellar discs, have the skills to run and adapt hydrodynamical simulations to be run in remote CPU/GPU clusters, and ideally have some experience producing synthetic
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 20 days ago
://ffaucher.gitlab.io/hawen-website/ ), by enriching its feature set and improving its parallel performance on graphical processing units (GPUs). The Hawen software simulates wave propagation in various media, including
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compute hardware and network infrastructure for the ITS Research services. This covers the 12000+ core, 100+ GPU QM High Performance Compute cluster (see https://docs.hpc.qmul.ac.uk ), various hosted