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Field
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HPC infrastructure; • installation, configuration, administration, and monitoring of: - CPU and GPU computing clusters, - storage systems, - virtualization and cloud computing platforms, - orchestration
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, automating, and optimizing the GPU and AI and computing infrastructure used by researchers across the university, along with the high-performance computing systems that support it. This position will be filled
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, automating, and optimizing the GPU and AI and computing infrastructure used by researchers across the university, along with the high-performance computing systems that support it. The position will be filled
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computation, GPU and differentiable programming, or AI-assisted scientific software development. The postholder will also develop GPU-accelerated Bayesian inference methods, including nested sampling, publish
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and ethically. Its core is a studio of high-performance GPU workstations hosting open-source models locally (image, video, audio and language) where staff and students can see, adjust and interrogate
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scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing predictive performance, generalisation
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turbulent flow simulations. 3. Background in numerical methods and scientific computing. 4. Proficiency in Python programming and high-performance computing on modern GPU-based platforms
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potentials or force fields (for example MACE, NequIP, DeePMD). ● Experience running scientific calculations on Linux-based high-performance computing clusters, including scripting or workflow automation
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CUDA kernels and/or Triton, and inference engines (vLLM, SGLang, et cetera). Experience coding with deep learning libraries such as Pytorch/JAX is essential. Applicants must hold a PhD in computer
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foundations. The Centre is committed to working with AI systems openly and ethically. Its core is a studio of high-performance GPU workstations hosting open-source models locally (image, video, audio and