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behaviour. However, most existing multimodal AI systems depend on large, computationally intensive models that are difficult to deploy in settings with limited resources. This PhD project explores a different
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Aim: To predict the rate of hair loss or recovery in people with alopecia using computer vision and Artificial Intelligence (AI) algorithms. Objectives: Automate the hair segmentation process and
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unit and then pre-processed data used as the input of the deep learning algorithm. The research will employ the SafeML tool (a novel open-source safety monitoring tool) to measure the statistical
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health. Policymakers allocate limited testing and surveillance resources across different locations, aiming to maximise the information gained about disease prevalence and incidence. This project will
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. Design and implement multimodal unlearning techniques to address bias and privacy concerns. Evaluate the generalisability of multimodal learning across different socio-contexts. Validate the proposed
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simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state-of-the-art simulation algorithms to circumvent
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CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical
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algorithmic techniques needed to generate reliable high order meshes for complex, multiscale industrial geometries. You will work within a technically focused research group that maintains regular interaction
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coupling, applications, and development of regional ocean models. Capitalising and contributing to this effort, this project will Investigate effective downscaling strategies for different regional ocean
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category