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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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developments; Significant experience with the development of custom modules using GPU-accelerated APIs for deep learning (e.g., Pytorch); and Publications in top-tier venues in Machine Learning and/or Signal
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theoretical physics, whose responsibilities relate to distributed systems and the GPU optimization of AI algorithms. We expect the team to grow in size considerably over the next few years, and are looking
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of parallel computing (GPUs) to speed solution within the optimisation process. Funding Notes 1st or 2:1 degree in Engineering, Materials Science, Physics, Chemistry, Applied Mathematics, or other Relevant
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/GPU architectures and even quantum computing are currently actively explored. Candidates are sought that align with these research areas. A broad interest in teaching topics would additionally be
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and GPU, as well as addressing relevant computational and algorithmic questions. Main duties: • To undertake research aimed at computational protein design by developing molecular simulation methods
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: Applications accepted all year round Details In this project you will combine state of the art software development approaches for GPU programming with the study of interactions of high-energy particles with
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as code” approach to systems automation. You’ll be working across a range of predominately Linux based systems, including HPC and GPU accelerated compute, large-scale and high-performance storage, and
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familiarity with Deep Learning, including PyTorch would convey a significant advantage. They will have access to our in-house GPU-enabled High Performance Computing platform as well as national HPC resources
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documentation, and promote the benefits of our HPC resources across the institution. Collaborating closely with academic staff, researchers, and IT Services, you will ensure the HPC and GPU facilities