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of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast estimation of 3D coronary hemodynamics (velocity, pressure, and wall shear
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learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level logical blocks
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unrealistic, and can therefore result in inadequate models. This project proposes a generalized framework for extremal structural causal models on arbitrary directed acyclic graphs. Our new models will be able
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representations, and flow matching) for uncertainty-aware 3D reconstruction of coronary anatomy from 2D X-ray angiography; develop physics-informed neural networks and graph-based neural operators for fast
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, Create insightful graphs and other graphical tools to present outcomes back to farmers, Develop reporting for specific groups of farmers that are for instance in a specific supply chain or a learning
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) Developing deep learning pipelines for multilayer segmentation and nanoscale transistor classification from microscopy images; (b) Designing graph-based inference models capable of reconstructing higher-level
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to provide reliable predictions and support effective decision-making. The aim of the PhD project is to address these gaps by developing theoretical and computational tools that combine game theory, graph
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people represent environments through concepts such as mental models, cognitive maps, and cognitive graphs. These approaches have provided important insights into how people perceive locations, learn route
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generalized framework for extremal structural causal models on arbitrary directed acyclic graphs. Our new models will be able to incorporate non-standard extreme directions, which permits the modeling
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mental models, cognitive maps, and cognitive graphs. These approaches have provided important insights into how people perceive locations, learn route layouts, and understand spatial relations. However