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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 5 hours ago
that scale to the full array. This scale up involves integration with cloud services, existing distributed storage networks, and the Array’s high-performance GPU accelerated pipelines. In collaboration with a
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- GPU. What we offer As well as the exciting opportunities this role presents, we also offer some great benefits some of which are below: 41 Days holiday (27 days annual leave 8 bank holiday and 6 closure
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these resources through a cloud-native Kubernetes environment integrating large-scale CPU and GPU resources, Ceph object storage, BinderHub, Coffea-Casa, Dask, and ServiceX. This platform supports more than 500
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of automation systems for software deployment, configuration management, CI/CD, and environment lifecycle processes. Partner with researchers and domain experts to optimize applications for CPU/GPU architectures
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of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant
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of central London. For more information: https://www.kcl.ac.uk/engineering About the role This role will support the delivery of a mesh generation project, funded under a recent major £7m EPSRC Programme Grant
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precision algorithms for CPUs and GPUs. Performance engineering and analysis including application profiling, benchmarking to identify performance bottlenecks. Verification, and validation of the developed
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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-following inverters. Implementing and optimizing scalable algorithms for transient and stability analyses on HPC architectures (CPU, GPU, hybrid). Enhancing the numerical robustness and efficiency of existing
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energy efficiency bounds of modern CPU, GPU and FPGA devices at performing set operations in the context of combinatorial applications; Investigation of current trends in programming FPGA accelerators and