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Field
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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on Graphs: Symmetry Meets Structure (LOGSMS). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing
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). The field of Machine Learning on Graphs aims to extract knowledge from graph-structured and network data through powerful machine learning models. Designing provably powerful learning models for graphs will
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- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute to a corpus of geo-analytical scenarios with
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to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and machine unlearning to identify and mitigate bias and shortcuts
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hybrid AI (combining machine learning, feature-based modelling, and classical OR); Designing intelligent release, workload control and material planning methods that stabilize flow, improve on-time
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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We’re seeking for motivated candidates that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon
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project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning