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Field
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, with experience in algorithm development. Confident coding skills and familiarity with modern coding workflows and best practices. Excellent verbal communication skills and the ability to engage
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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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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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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
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point to sites of potential malformation (even before they form). The RA on this project would be required to develop and train novel AI algorithms for extracting surface models of the fetal cortex and
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to test the current algorithm and inform development of future algorithm refinements aimed at supporting diabetic foot ulcer (DFU) prevention through identification of temperature differences of > 2.2°C
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mix of algorithm engineering and formal methods, alongside more traditional software engineering activities. This project involves developing software which is both mathematically rigorous, and
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guiding the development of advanced impact-resistant materials. The main purpose of this role is to develop multi-physics computational models and reveal liquid-solid impact damage mechanisms. The post will