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candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use this experience in collaboration with
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turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use
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language requirement of the UK HEI; Have a background or a proven interest in AI foundations and its application in civil and environmental engineering, including machine learning, sustainable construction, climate
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programming language Experience with statistical inference or machine learning methods (e.g. ABC, Bayesian modelling) A proven publication record with at least one first author publication in a peer-reviewed
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the next generation of gas turbine engines. Successful candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods
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of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience of C/C++/C#, TensorFlow would be beneficial
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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: Erlangen Programme for AI” This is a 5-year programme supported by the EPSRC and is a collaboration of mathematicians and computer scientists at the University of Southampton, the University of Oxford (lead
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and programming skills and experience of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience
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of Birmingham is inviting applications for a Research Fellow position focused on Machine Learning for Automated Formal Verification. Machine learning has transformed programming, with code generation rapidly