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Full time, fixed term until 31st January 2028 Opportunity to lead and shape a flagship, university-wide AI and GPU compute capability supporting cutting-edge research Base Salary $162,528 + 17
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for GPU-based AI workloads, with control at the level of transformer blocks and attention layers. • Design and build a high-resolution (microsecond-scale) performance monitoring framework to capture
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architectures and embedded platforms (ARM Cortex-M, NPU, FPGA, embedded GPU), e.g., via academic courses and/or project courses Research experience (e.g., through a Master thesis work or research internships) is
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opportunity for someone seeking to develop their knowledge and skills in system administration, service development and technical support. In this role you will assist with management of the School’s GPU
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opportunity to advance your academic career at a world-leading institution dedicated to using science for the benefit of humanity. Access to state-of-the-art facilities, including extensive departmental CPU/GPU
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clusters, GPU-enabled systems, job schedulers (e.g., Slurm), and parallel computing workflows supporting simulations, bioinformatics, machine learning, or large-scale data analysis Experience managing
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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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. We train open foundation models with hundreds of billions of parameters on thousands of GPUs on one of the largest AI-ready supercomputers in Europe. The team counts more than a dozen full-time
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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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to substantial in-house H200 GPU infrastructure, further capacity through the Norwich Data Centre and routes to national-scale AI compute such as Isambard-AI. Collaboration with the Earlham Biofoundry will provide