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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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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | about 1 month 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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networking technologies such as InfiniBand. Working knowledge of GPU technologies like CUDA and OpenCL. Experience with distributed computing job schedulers (e.g., Slurm, PBS). Familiarity with
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Inria, the French national research institute for the digital sciences | Pau, Aquitaine | France | 2 months ago
coupling with adaptive meshes. High-order spectral finite element methods (SFEM) offer superior accuracy per degree of freedom and are naturally suited to HPC architectures (CPU/GPU clusters). Two main
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of research computing at LSE. Your expertise will be key in future-proofing our research hardware environment, ensuring high availability, scalability and security across HPC clusters; GPU acceleration, high
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, versioned RESTful API (e.g., FastAPI/Flask) for inference; and Containerise services (Docker), set up CI/CD pipelines, and deploy for inference on GPU/CPU servers. Data engineering, Governance, and
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and diffusion models, LLMs, VLMs, LAMs, and world models, and fluency in tools for AI/real-time/graphics pipelines (e.g., Python, PyTorch, C++, GPU/compute, networking). Base location: Pinewood Studios
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, development and implementation of machine learning models Preferred skills/knowledge includes: Active TS/SCI Clearance strongly preferred A Master’s degree Training and optimizing ML algorithms on GPU hardware
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, including cloud computing, cluster computing, or GPU-based acceleration; Excellent communication skills with a problem-solving mindset and the ability to work both independently and collaboratively with cross
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when necessary. Work closely with ODFM to facilitate request for office and lab access. Manage any IT matters, including IT equipment allocation and support for GPU servers (if required). 2. Support on