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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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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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control for manufacturing operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine
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to treatment, population health monitoring, workforce development and leadership, policy, and advocacy. Background The Robotics, Autonomy and Machine Intelligence (RAMI) Group led by Prof Nabil Aouf is dedicated
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from Deepfakes Project. We are looking for a software/machine learning engineer (or similar) to work in an interdisciplinary team reporting to Dr Sophie Nightingale (Principal Investigator). The Project
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tools, collaboration with project stakeholders, and engagement with the consortium and Defence and Security stakeholders. Technical Requirements: Strong coding skills with background in machine learning
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Introduction As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill
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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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: 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