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, you’ll join a technically driven, publication active team known for research in computational modelling, CFD, numerical methods and high-performance computing, with a strong culture of code quality, open
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evaluate numerical methods that combine low and high order formulations, and develop hybrid discretisation frameworks that apply varying fidelity across the computational domain. This includes creating
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Performance Computing, computational and cutting-edge data analytics methods, and growth and retention of vital capacity in research software engineering and technical R&D are at the core of this endeavour. We
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from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with experimental data from both literature and in
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technical knowledge in areas such as: Foundational Models Algorithmic Research Machine and Deep Learning Computer Vision Edge AI, TinyML, and Embedded AI Explainable AI Safe AI Federated, Parallel
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interdisciplinary collaborations. Leading-edge platforms including High Performance Computing, computational and cutting-edge data analytics methods, and growth and retention of vital capacity in research software
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provide powerful tools to improve the quality and efficiency of data-driven models. In parallel to the development of data-driven models for dynamical systems with geometric structures such as Hamiltonian
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, and a solid understanding of numerical analysis and familiarity with the use of analytical tools. They should also have knowledge and experience in parallel coding and spectral methods. They must have
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Shifting bits: Adaptive numerical precision for GPU software in particle physics and beyond (S3.5-COM-Richmond) School of Computer Science PhD Research Project Competition Funded Students Worldwide
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Performance Computing, computational and cutting-edge data analytics methods, and growth and retention of vital capacity in research software engineering and technical R&D are at the core of this endeavour. We