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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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will contribute to projects focused on developing advanced machine learning models to quantify phenotypic traits of crops, including corn, soybean, and other selected species. These models will leverage
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). The candidate should have hands-on experience developing state-of-the-art machine learning models, particularly deep neural networks (experience with graph neural networks is highly valued). Their background
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configurations and illumination conditions. Implement and validate device-independent representations. Investigate and apply domain adaptation and transfer learning techniques to develop models that generalize
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built to identify and correct errors, apply bias adjustments, and assess data quality. State-of-the-art multisource blending methods will then be applied (e.g. kriging, probabilistic merging, machine
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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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van Erven. This is what you will do AI and machine learning models keep getting better, but how they make their decisions often remains unclear, because these depend on many incomprehensible model
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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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attachment. Main tasks Develop machine learning models to produce forest information at local and landscape scales Develop machine learning models for sustainable forest management Compare machine learning