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Primary supervisor - Prof Kate Kemsley Join us to research and develop advanced analytical methods for tackling food fraud head-on! Economically motivated adulteration of foods is a significant
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computational scientists to develop advanced algorithms and models. Research and validate potential biomarkers associated with breast cancer progression, treatment response, or patient outcomes, using
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have a focus on Quantum Circuits, Quantum Algorithms, and preferably expertise in developing Software for Quantum Machines.The appointment will be for a period of two years, and extensions to longer
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development with Python Experience of machine learning with PyTorch Good knowledge of machine learning and computer vision algorithms Ability to work on own initiative and in a team Experience in collaborative
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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include supervision of projects, teaching courses at bachelor's and master’s level, and examination as well as possible supervision of PhD students. Furthermore, you should contribute to the development
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objective is to develop a next generation of AI approaches that are more sustainable and accessible. Relevant domains include mathematical and computational optimization, learning algorithms, statistical
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of stroke patients and healthy volunteers. Developing algorithms for identifying and excluding motor unit filters associated to impaired motor units. Integrating real-time-decoded features of motor unit
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multi-agent autonomous systems and related technologies. This will include development of distributed monitoring algorithms enabling agents in a multi-agent swarm to autonomously locate other agents in