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promising designs will then be validated experimentally, first in a controlled academic prototype and subsequently on a lithography scanner stage demonstrator in collaboration with ASML. The PhD is supervised
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enablers for predicting and reasoning about dynamics and transport patterns, in turn being of key importance for a broad range of human activities. The research in the PhD project will focus on core spatio
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apply AI and data-driven modelling to predict system efficiency - balancing air purification with energy consumption. It will also explore how sensor feedback can control treatment systems and communicate
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biomarker data. Develop predictive models and algorithms to identify risk factors, disease markers, and potential therapeutic targets for Alzheimer’s disease. Implement machine learning models to improve
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be for 36 months, under the supervision of Dr Rea Antoniou-Kourounioti and Prof Matt Jones. The research associate will develop predictive and testable mathematical models to simulate plant cold
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. This PhD proposal aims to develop an integrated modelling-prediction-control framework that uses extreme-weather-aware AI to coordinate frequency stability, voltage control, optimal power distribution, and
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controlling their noise is critical. This PhD focuses on airborne noise source localization in urban environments, enabling quiet air mobility. Job description The rapid growth of air mobility operations