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The University of Vienna is a community of over 10,000 individuals, including approximately 7,500 academic staff members, who passionately pursue answers to the profound questions that shape our
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Networks (PINNs) perform when tackling these tough equations. We’re diving into key questions such as: How does the presence of a small parameter affect the number of training points needed? How complex does
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scientific and societal challenges. The selected candidate will contribute to an exciting project at the intersection of data assimilation and optimal transport, focusing on Wasserstein Gradient Flows
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and procedures for 3D inspection and measurement systems. You will research on innovative computer vision methods and tools using advanced mathematical methods, optimization methods and AI methods and
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international partners and networks and become familiar with initiatives similar to the ECH. You develop metrics for success and benchmarking frameworks to monitor and optimize all Hub programs and performance in