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of research computing at LSE. Your expertise will be key in future-proofing our research hardware environment, ensuring high availability, scalability and security across HPC clusters; GPU acceleration, high
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has embraced the “infrastructure 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
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patient records exploiting HPC, including GPUs embedded within NHS infrastructure. Development and deployment of ML operations software and tooling for ML / LLM algorithms working over free-text clinical
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suite of software, and its deployment on the university HPC & GPU based system. The position is primarily research and enterprise, but there would be a contribution of up to 20% to teaching, including
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GPUs). Research Associate: Hold a PhD in high performance computing, computational fluid dynamics or a closely related discipline*, or equivalent research, industrial or commercial experience. Research
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of code acceleration (GPU) Participate in numerical modelling (HPC (GPU), MPI Fortran / C, C++ Kokkos, Python, Perl) of SAMS front end and physics/test modules. Write research reports, progress reports
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-performance computing, including parallel or GPU programming (MPI, OpenMP, CUDA, Kokkos, etc.) Familiarity with modern software development practices, including debugging, profiling, and version control
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well as access to the group dedicated computing cluster environment with H100, L40s, and A40 GPUs. This post is funded by the UKRI Future Leaders Fellowship, a flexible long-term public funding scheme
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Selectris energy filter, Vitrobot Mk. IV, GPU- and data storage-cluster and ready access to Titan Krioses at the Francis Crick Institute, LonCEN consortium, and the UK national cryoEM facility at eBIC. What
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free text of both biomedical literature and electronic patient records exploiting HPC, including GPUs embedded within NHS infrastructure. Development and deployment of ML operations software and tooling