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GPU acceleration (CUDA) Participation in relevant competitions (e.g., Kaggle, computer vision challenges) Experience with version control (Git) and collaborative development practices
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Implement sustainable and reproducible and FAIR research software engineering practices Collaborate with other HPC facilities and project partners Help evaluate and integrate GPU acceleration and other modern
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infrastructures, improving system performance, scalability, and efficiency by optimizing resource usage (e.g., GPUs, CPUs, energy consumption). Researchers and students will explore innovative approaches to reduce
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facilities. Core responsibilities include the deployment and maintenance of small-scale HPC and compute nodes, GPU workstations, Linux and Windows servers, research data storage and backup solutions
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managing all underlying compute, networking, and storage resources (e.g., Kubernetes clusters, GPU instances). Feature Engineering: Define shared data and feature management patterns to ensure consistency
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/CloudFormation) for provisioning and managing all underlying compute, networking, and storage resources (e.g., Kubernetes clusters, GPU instances). Feature Engineering: Define shared data and feature management
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storage and archiving solutions to collaboration and analytics tools. ARC also delivers Baskerville; a leading GPU accelerated National Compute Resource (NCR) and supports researchers using specialist
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to apply; we value employees with a willingness to learn. Understanding of technologies employed in research in higher education Familiarity with distributed computation solutions Familiarity with GPU
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to apply; we value employees with a willingness to learn. Understanding of technologies employed in research in higher education Familiarity with distributed computation solutions Familiarity with GPU
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computing environments for advanced research. B2 Knowledge of scaling and optimising software to take advantage of GPU / HPC infrastructure. Skills Essential: C1 Excellent written and verbal communication