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materials, to aid design of novel more energy-efficient processing routes. The development of these digital twins requires reliable and predictive models for microstructure formation during steel processing
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of these digital twins requires reliable and predictive models for microstructure formation during steel processing. These models should be based on accurate predictions of phase stability and defect kinetics
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continuously and accurately after surgery to measure and evaluate patients’ recovery progress, and timely detect and even predict clinical adverse events like delirium, cardiac arrhythmias and pneumonia. In
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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective
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well as reservoir containment. Objectives: Design ensembles of reservoir models for UHS that capture the different scales of heterogeneity and their associated geological and conceptual uncertainties. Designing
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mixing using pipettes/scales, and handling instruments and equipment. You have a quantitative mindset with the ability to analyse data, make predictions, and perform back-of-the-envelope calculations. You
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, pre-operative determination via endoscopies and imaging remains unreliable for patient selection. The objective of this study is to develop an image-based machine learning prediction model to assess
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measurement data for translating and testing model predictions; bioinformaticians, who investigate evolutionary conservation of sequence, (co)expression and regulatory modules; and modellers, who develop crop
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been learned from data. For instance, why does a machine learning model predict that it is unsafe to discharge a certain patient from the intensive care? Or which characteristics make a machine learning