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Field
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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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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
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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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an advantage. To address the latter challenge, the PhD candidate will build on Halbertsma et al. (2020) and Elshout et al. (2021) to develop and validate functional MRI-based biomarkers capable of predicting
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new survey data about attendance, and analyzes longitudinal data on participation. Examples of questions that will be answered are: How can we disentangle age and cohort effects in order to predict
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sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models that can accurately predict component health and
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sources and probabilistic reliability analysis to predict both current and future safety levels. This project contributes to designing future standards and safer vertical transport. Information
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. Process simulation software is being developed for virtual optimization of tool design and material handling, enabling first-time-right manufacturing. The predictive quality of these tools relies
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improve prognostication in critically ill patients by integrating cardiovascular risk factors into existing ICU prediction models. by combining cutting-edge data science with advanced clinical expertise and