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Knowledge of machine learning or multi-omics data integration would be highly desirable Essential Application/Interview Deep interest in musculoskeletal research and translational science Essential
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. This project will develop responsive manufacturing technology that will have sufficient flexibility to overcome such problems by utilizing intelligent machine learning to control the printing process in real
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are looking for an ambitious candidate with a strong background in mathematical and statistical methods for both physics-based modelling and machine learning, and their application to engineering problems in
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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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preferences for them using birds as a model system. Capitalising on recent advances in computational neuroscience and machine learning, specific objectives are to (1) quantify common design features of avian
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Knowledge of workshop activity: for example vacuum technology, metrology, material finishing, CNC and conventional machining, welding (particularly TIG). Desirable Application/interview Experience with
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from level 3 through to level 7. In this role, you will be required to develop teaching resources using a variety of active learning strategies, to ensure the apprentices have a high-quality learning
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greener transportation and energy. Building on recent advances, the successful candidate will use a powerful combination of dynamical systems theory, optimisation, DNS and machine learning to model and
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who is also skilled in bioinformatics, image analysis, and machine learning. You’ll be part of a dynamic, supportive, and forward-thinking research environment committed to making real clinical impact
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extraction, as well as the model feature and machine learning based TCM into the framework of digital twins. This allows building up and updating a digital twin of machine tool dynamics via a completely data