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Field
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repaired, reused, or discarded requires sophisticated condition assessment and decision-making capabilities. This PhD project tackles a critical challenge: how to develop robust machine learning models
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of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to invent, develop and evaluate novel methods for pre-training and fine-tuning of perceptual foundation
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identification and machine learning. The key challenge is striking a balance between, on the one hand, modelling the physical, dynamic and nonlinear behavior of the components with sufficient physical accuracy
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-tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to
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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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Candidate Human-Centered Interpretable Machine Learning (1.0fte) Project description In recent years, practitioners and researchers have realized that predictions made by machine learning models should be
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, practitioners and researchers have realized that predictions made by machine learning models should be transparent and intelligible. Although explainable AI methods can shed some light on the inner workings
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years, practitioners and researchers have realized that predictions made by machine learning models should be transparent and intelligible. Although explainable AI methods can shed some light on the inner
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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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, 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 model flag a certain bank transfer as