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for anomaly Detection and diagnostics: Leveraging state-of-the-art machine learning and deep learning models for automated fault detection, classification, and time-till-failure prediction. This will involve
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variable, even within the same family, making it difficult to predict the course of disease, provide accurate genetic counselling, or design effective therapies. This PhD project aims to better understand
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. The core objective is to develop advanced 3-D modelling and optimisation methodologies for magnetic components that enable accurate leakage inductance prediction and improved overall performance. Traditional
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, prognosis and therapy response prediction of cancer patients. Liquid biopsies are now offering a great potential for minimally-invasive exploration of circulating tumor nucleic acids and cells. However, some
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load predictions for wind turbines, specifically the foundations, with the ultimate objective of including structural health information in windfarm asset management to optimise structural lifetime
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. Defining predictive tasks based on clinical goals. Selecting and setting up appropriate data preprocessing pipelines. Training and evaluation of computer vision models. Internal and external algorithmic
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disease is highly variable, even within the same family, making it difficult to predict the course of disease, provide accurate genetic counselling, or design effective therapies. This PhD project aims