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environments, Slurm/Slinky workloads, Kubernetes/OKE services, Open OnDemand, GPU and CPU partitions, and shared storage. Help provision, configure, scale, and validate compute, storage, networking, and platform
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for advanced research. A7. Knowledge of scaling and optimising software to take advantage of GPU / HPC infrastructure. Desirable: B1. Knowledge of Trusted Research Environments out with or within an HPC
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datasets. Experience with GPU-accelerated computing or high-performance computing environments. Experience contributing to research proposals, supervising students or working within international
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methods on accelerated or heterogeneous computing platforms, including GPUs, FPGAs or other specialised hardware, for real-time or large-scale physics applications. We welcome candidates whose work is
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expertise, and substantial GPU and high-performance computing resources. You will be encouraged to contribute ideas, shape new analytical directions and participate in publications, conference presentations
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research. BOLD also has access to national compute resources (3M GPU hours on Isambard for the first 1.5 years) and we are working hard to get to 5000 H100 equivalent compute capacity in total across
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research. What about computing power? Well, BOLD also has access to national compute resources (3M GPU hours on Isambard for the first 1.5 years) and we are working hard to get to 5000 H100 equivalent
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have access to substantial AI compute, including in-house state-of-the-art H200 GPU servers, alongside further capacity through the Norwich Data Centre and access to national-scale AI compute through
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experience with probabilistic or computational modelling. Experience with language model evaluation, cognitive modelling, reinforcement learning, goal-directed behaviour, learning theory or large-scale GPU
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written communication skills Experience with GPU training and handling large medical datasets e.g., large magnetic resonance (neuro)imaging datasets. Basic understanding of radiology clinical workflows and