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Field
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, numerical optimization, numerical partial differential equations, and parallel computing. The Researcher will join a project developing parallel high-order meshing algorithms from medical images and parallel
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and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
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, including hybrid simulations coupling machine learning with numerical methods, multiscale discretization, nonlocal closure modeling, structure preservation, multilevel and multifidelity machine learning
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in a dynamic and collaborative team. In collaboration with the Edinburgh Parallel Computing Centre (EPCC) and our industry partners, the focus of the role is the development of a new solver for
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and tuning. Moderate research project experience training large-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain
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), numerical methods, and basic knowledge of material science. It is meriting to have one or more of the following skills/qualities: experience and/or thorough understanding of theoretical/numerical methods
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-scale foundation models, especially pipeline/model parallelism. Track record of creating HPC software for numerical methods. Domain expertise in areas like computational fluid dynamics, material
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lattice field theory and numerical methods, with experience in HPC programming (e.g., C++, Python, MPI, OpenMP, CUDA) and parallel computing environments. - Experience in performance analysis, debugging
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applications in real world data sets. We are also interested in exploring opportunities for parallelism of the completion process, highlighting the potential for significant speedup in computations. Job
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schemes for their discretization Knowledge of numerical optimization Knowledge of Python, Matlab and FOTRAN90 compiled code, use of high-performance parallel computing servers Ability to synthesize and