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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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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
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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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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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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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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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machine learning We offer: Academic freedom to pursue your scientific interests related to infection biology, inflammation, gene expression, and intracellular organization Competitive salary including
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. Advanced modeling techniques, such as surrogate modeling, machine learning, and physics-informed neural networks, will be applied to accelerate simulations and enable real-time performance. A strong emphasis
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computer vision models for forest-based 3D point cloud data. In recent years, large advances have been made for deep learning algorithms for high-resolution point clouds from small geographic areas. We seek
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, supervised by Dr. Tim 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