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Field
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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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, post-docs and interns collaborating across universities to build better algorithms, software tools and benchmarks to assess the safety of AI implementations at the software and hardware level. We
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Award summary This studentship provides an annual living allowance (stipend) of £21,470, and full tuition fees (Home fee level only). Overview This project will develop uncertainty quantification
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of fingers, the shapes of the fingers, and the positions of tactile sensors), and the control policy for that hand, when given a particular task or set of tasks. Through this, we aim to develop a framework
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. The task of the theory group led by Prof Kyriienko at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will
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field combines cutting-edge engineering with sustainable development, making it highly relevant to both technological advancement and humanitarian impact in today's interconnected world. This project aims
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This doctoral research will focus on the development, optimisation, and coordinated deployment of advanced aerial platforms, specifically electric vertical take-off and landing vehicles (eVTOLs) and
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formulation, which displays striking similarities to that used by the Computational Fluid Dynamics (CFD) community, has inspired the investigators to adopt conventional CFD algorithms in the novel context
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scientific publications, patents, and seeing collaborators translate our work into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and
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into real-world settings. You will be responsible for developing machine learning and AI algorithms for a range of data and applications (e.g. natural language processing, multivariate time-series data