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the infiltration of pollutants into car cabins (and thus reduce the exposure of the occupants) or to identify areas where they can accumulate (areas to be avoided by pedestrians and cyclists) depends on these issues
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connectivity and flight dynamics, incorporating Artificial Intelligence (AI) and Machine Learning (ML) to develop innovative prediction mechanisms. The primary goal of this project is to design advanced
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is mainly computer based. Previous experience of computerised accounts systems is also essential, significant experience of the UNIT4 Business World (Agresso) system would be desirable. About Us As a
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is mainly computer based. Previous experience of computerised accounts systems is also essential, significant experience of the UNIT4 Business World (Agresso) system would be desirable. About Us As a
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allow the student to acquire expertise in electro-magnetic propagation and sensing with electro-optical (EO), infrared (IR) and radar systems. Overview The project is to contribute to a major Ministry of
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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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will be combined to generate a numerical tool to predict rupture of materials under blast loading. Machine Learning techniques will also be investigated throughout the project to rapidly assess the large
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learning from in-service vehicle fleets and predicting remaining useful life. Applications of artificial intelligence and computer science to battery state estimation. Reduced-authority control of hybrid
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diagnosis and prognosis technologies, and, consequently, improve maintenance decision making. Currently, machine learning exists as the most promising technologies of big data analytics in industrial problems
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with other sensing approaches, such as from airborne, satellite platforms and other on-site data sources. 4. Develop edge-based machine-learning techniques to provide near-instantaneous emission