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, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced control theory (e.g., model-predictive control, etc
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motivated Research Associate/Fellow in Electrical Machines to join the University of Nottingham's Power Electronics, Machines and Control (PEMC) group — one of the UK's largest and most ambitious research
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learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor crash predictions
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artificial intelligence and machine learning. The postdoctoral fellow will contribute to the development of a comprehensive, multi-modal framework for predicting and managing cardiovascular disease by
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-age-gap prediction algorithms applicable to Asian populations by integrating large-scale plasma proteomic datasets generated using the Olink and SomaScan platforms. The organ age gap is defined as the
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will focus on the development of AI-enabled programmable coherent fibre-laser arrays. You will investigate how the spatial properties of coherent optical fields can be measured, predicted and controlled
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prediction of regional climate and extremes using hybrid physics-AI models. About the project/work tasks There is a growing need for subseasonal-to-seasonal (S2S; 2 weeks to 12 months) predictions of regional
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adaptation with an urgent applied challenge: predicting the evolutionary sustainability of biological control. Extended research stays in Brazil will be central to the project, including close collaboration
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representation of snow and glacier physical processes and ability to predict cold regions hydrology. The researcher will use the observations from the Canadian Rockies Hydrological Observatory of Global Water
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Strong background in control, robotics and machine learning, and experience in areas like model predictive control, adaptive control, reinforcement learning, robot perception or manipulation. Programming