46 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" uni jobs at University of Sheffield in United Kingdom
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse
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EngD: Physics-based AI for Intelligent Machining: Learning, Optimisation, and Uncertainty in Next-Generation CAM Software (sponsored by DigitalCNC) EPSRC Centre for Doctoral Training in Machining
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artificial intelligence/machine learning may be applied are encouraged to apply. If you are interested in research in brain-machine interfaces, and are unsure about whether you have the right background
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, and in-depth data analysis? We're looking for a fast-learning individual with strong transferable research skills to join our Digital Machining team as a Project Engineer In this role, you'll be
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between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective
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parallel processing, FPGA coding and analysis, along with Machine Learning and AI based image analysis. The final aim of the project will be to generate in-situ / live film profile data to coating line
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BSM processes. This will involve taking a lead role in developing dedicated software frameworks, including the implementation of machine learning techniques. A long-term attachment (6-12 months) and
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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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the third Higgs boson decays to two tau leptons, or another highly sensitive combination. The student will gain expertise in machine learning techniques for signal-background discrimination and will