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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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statistical methods are not suitable for big data due to their certain characteristics: heterogeneity, statistical biases, noise accumulations, spurious correlation, and incidental endogeneity. Therefore, big
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
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correction. This machine-learning approach, however, needs a realistic model of light propagation in the retina in order to validate it and to generate the large volumes of training data required. Funding
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) and the University of Warwick seeks to develop flexible digital twin models that will enable novel, integrated solutions to the decarbonisation of heating and cooling on large industrial and commercial
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. Experience in working with large data sets, knowledge of statistics, and some programming expertise is essential. The project is based in ECEHH, at the University of Exeter’s Penryn Campus in Cornwall, and may
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. Novel techniques, such as microscopy, fluorescence microscopy, and light-field imaging allow users to gather more useful information with higher levels of details than ever imagined. However, lots of
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. Novel techniques, such as microscopy, fluorescence microscopy, and light-field imaging allow users to gather more useful information with higher levels of details than ever imagined. However, lots of
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-developing the principles (e.g. ethics) and methods for anonymising, processing and analysing sensitive data collected by a national charity’s 24/7 helpline for people experiencing or witnessing elder abuse
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engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early