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, this interdisciplinary project will couple mathematical models of earthworm movement, stochastic models of the measurement process and designed experiments to improve earthworm detection. Project This project will work
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objectives: To co-produce actionable knowledge on heat-related risks and adaptation strategies with policy partners, civil society, and citizens. To estimate the effect of heat exposure on child mental health
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on Machine Learning and Psychophysiological Deception Detection. The studentship is part sponsored by GCHQ and funded for up to 3.5 years with fees and a stipend at the standard UKRI rate. The position is only
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impacts of exposure to heat in primary school-age children in Accra, Ghana and leverages extensive existing research infrastructure, data, and partnerships. It is designed around three core objectives: To
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker