52 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" uni jobs at University of Sheffield
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physics-based, and data-driven AI-based approaches employing neural-networks and machine learning, this project will develop and validate a multi-time scale DT concept for advanced condition monitoring and
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(Python, C++). - Knowledge of AI, machine learning, control systems, or reinforcement learning. - Ability to work independently, communicate effectively, and contribute to collaborative research. Desirable
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as the Insigneo Institute theme co-director for Healthcare Data/AI and the N8 Centre of Excellence in Computationally Intensive Research theme lead for Machine Learning. About the School/Research Group
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on machine learning for NLP, with applications in education, creativity, healthcare, social media, and finance. She specialises in educational NLP, EdTech, language acquisition, multilingual and low-resource
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, combining the radio frequency (RF) circuitry of a transceiver with the digital processing needed for machine learning, all in a single microchip. Next generation wireless devices will not only send and
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Experience in developing software to a high standard using a range of computer languages and tools, ideally for applications involving the modelling, simulation and analysis of the large, complex and dynamic
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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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Machining Group and others as required. You will aid and advise in the creation, definition and maintenance of safety and operational procedures. Subsequently, you will be responsible for ensuring
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Design of a Fault Detection System for AI-Assisted Adversarial Attacks on Industrial Control Systems
AI-assisted adversarial attacks. You will work on topics such as cybersecurity, intrusion detection, adversarial machine learning, industrial automation, digital twin technology, and reinforcement
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round Details This project explores how machine learning and artificial intelligence can transform the scholarly digital editing process, not only by potentially automating and enhancing editorial