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development, machine learning and signal processing, and system integration. We are interested in working on different areas to improve the BCI technology. These areas include (but are not limited
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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
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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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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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the team ● Provide support for approved researchers, including validating data, spinning up virtual machines for research groups and enabling access to the relevant data as appropriate, providing support
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tested in controlled, structured synthetic environments. This approach generally leads to their spurious adoption in clinical practices. With the advances of machine learning (ML), AI and virtual reality
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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