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of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or experimental means. The PDA is expected to actively disseminate results through publications in
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. Applicants should have strong expertise in computational analysis of electrophysiological data as well as proficiency in large language models and machine learning algorithms. First-hand experience in
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systems, multi-function radar, AI/ML algorithms rely on high performance digital signal processing and real-time computing to provide high-fidelity results in an actionable timeframe. The ARRC intends
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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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in algorithms development and refinement; (b) a good command of both written and spoken English; and (c) at least two first-authored publications. Preference will be given to those with: (a
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or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
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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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developing a machine learning (ML) algorithm for the automated analysis of the above-mentioned mass spectra. Desirable: - knowledge in the field of Planetary Sciences - very good written and spoken English (C1
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Prof. Daniel Merkle. The overall research project is based on the novel application of formalisms, algorithms, and computational methods from computer science to the design of microbial communities
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the supervision of Prof. Muhammad Ali Imran and Prof Jonathan Cooper, who will act as Line Managers. The project particularly emphasizes ambient and remote sensing techniques, connectivity for healthcare, and