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for donor kidneys. Central to this is the use of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies
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that can accurately predict component health and optimize repair decisions. You will develop novel data-centric approaches to Remaining Useful Life (RUL) prediction that goes beyond traditional model-focused
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vulnerable to the use of recycled material, with possibly uncontrolled or variable content. Accordingly, this research project aims to establish predictive insights between microstructures contaminated with
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to neighborhood outcomes. In close collaboration with local stakeholders, experiments will be conducted to test model predictions and interventions. During the PhD, you will: Gain expertise in theory-driven
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, prediction, lifestyle change and pharmaceutical therapy. Unfortunately, there is a major and critical lack of effective implementation, which consistently fail to reach individuals that would benefit the most
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are looking for an enthusiastic and ambitious PhD student to join our interdisciplinary team. You will contribute to an innovative approach that aims to identify predictive bio-markers of epilepsy treatment
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assessment and certification framework, leveraging multiple data sources and probabilistic reliability analysis to predict both current and future safety levels. This project contributes to designing future
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of machine learning to evaluate the predictive value of biomarkers from various sources: donor-related data, perfusion fluid, and kidney biopsies. Kidney biopsies may contain unique information about organ
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the pavement sector presents new opportunities for improving remaining lifetime predictions. However, developing a future-ready approach requires systematic data handling to establish a clear link between
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management. Job description The increasing accessibility of data in the pavement sector presents new opportunities for improving remaining lifetime predictions. However, developing a future-ready approach