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, disease management, and therapeutic development. The collaborative PhD project, co-hosted by the Rademakers and Sleegers labs, focuses on the development of innovative genetic risk scores to predict
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with type 1 diabetes. This allows us to study the involvement of neutrophils in both blood and pancreatic tissue and to assess their potential as effector, predictive, and therapeutic targets. In
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of hyperthermal treatment and conventional anticancer therapy and discover potential biomarkers that can predict sensitivity to heat treatment. You will publish the scientific findings in peer-reviewed
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, that combines diffusion and transformer models, there are clear indications that the analysis of this data can be automated. This will open new avenues in data interpretation and building predictive models
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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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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