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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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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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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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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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projects simultaneously. F3 Experience supporting application development for a variety of systems, e.g. Windows, Linux, MacOS, Android, iOS and hardware, e.g. GPU programming. E.g. with the data management
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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
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showcases and visualisations; and scaling and optimising data science and AI research to leverage HPC and GPU infrastructure. This post is full time and open ended (permanent) Apply online at: Research
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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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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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: 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