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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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), 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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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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groups, and a laboratory for research and innovation in the application of advanced computational and data intensive research methods, working in partnership with academics from all fields. We are a home
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learning Demonstrated expertise in software and algorithm development, computational methods, data analysis, modeling, machine learning, high-performance and parallel computing, or scientific simulation
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computational methods with a particular focus on deep learning and image analysis. The research is done in close collaboration with the BioImageInformatics Unit of SciLifeLab . SciLifeLab is a national resource
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documented experience in at least one and preferably more of the following areas: computational solid- and/or biomechanics; finite strain hype elasticity; finite element methods; numerical optimization methods
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government’s Advanced Modular Reactor (AMR) programme has recently identified HTGRs as the preferred design for future advanced nuclear deployment in the UK, with an aim to deliver a demonstration reactor by the