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Application Deadline: Applications accepted all year round Details Self-driving laboratories (SDLs) combine the power of artificial intelligence (AI) and machine learning (ML), robotics, and automation
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in the world and will develop skills in machine learning, observational and theoretical astrophysics. For more information on this project please contact s.littlefair@sheffield.ac.uk Information
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and techniques. In addition, you will combine study and work-based learning to achieve the National Apprenticeship Standard - Laboratory Technician Level 3, which will span the full two years of your
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to AI, including deep reinforcement learning, human-in-the-loop machine learning, and multi-agent systems. Dr. Robert Loftin is a Lecturer in Machine Learning at the University of Sheffield. He received
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science, computer vision, medical/image analysis is essential. Experience of research (or interest in) in one or more of the following: deep learning; big data management; computational pathology; medical imaging
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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explore data-driven methods including machine learning (ML) and artificial intelligence (AI) techniques, to develop predictive HMPM tools that can diagnose, detect, and predict faults in machinery
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). Modelling tools used will vary according to application but are likely to including process simulation using Population Balance Modelling, DEM simulations and Machine Learning Approaches. Main duties and
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project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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data gaps by combining process simulation (e.g., Aspen software) with machine learning techniques. By developing accurate, large-scale life cycle inventory data using enhanced digital tools like deep