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of GPUs and/or time in either training or inference procedures, which pose considerable challenges to both academia and industry for widespread access and deployment. In particular, the sampling process of
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nature of electroweak symmetry breaking and mass generation in the standard model. We developed state of the art (open source) software working on GPU- and CPU-based supercomputing architectures, and
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analysed in a standard web browser. HiPIMS computations can be executed on local GPUs or through cloud-based GPU services, empowering users to conduct large-scale fast flood simulations without worrying
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GPU NVIDIA H100. Interdisciplinary collaborations with AI researchers and clinicians from NHS hospitals. Participate in large collaborative project funded by the National Institute of Health and Care
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for collaboration inside and outside of the University. It has access to extensive dedicated computing resources (GPU, large storage). The successful applicant will work under the supervision of Prof. Hain. Please
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-fidelity simulation data will be used to build the machine-learning model to be then embedded into a GPU-accelerated blade-element momentum solver that will be made open-source to enable direct impact with
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us to run large numerical simulations with billions grid points on mixed computer architectures including CPU and GPU machines. A current project is preparing the code set for the next generation of
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national high-performance computing facilities (both CPU and GPU-based) to conduct large-scale simulations efficiently. Working closely with experimental collaborators to validate computational predictions