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. Your tasks: Development and comparison of data driven models for the prediction of stresses in arterial walls and plaque Enhancing the models with physics, i.e., using different physics-aware machine
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algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical
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results and to make parameter estimation more efficient. The project will apply and evaluate these new methods at different sites and time periods, compare them with established approaches, and finally
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arterial walls and plaque Enhancing the models with physics, i.e., using different physics-aware machine learning models from the field of scientific machine learning Exploiting large language models
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. Your tasks in detail: Become familiar with our previously developed neural network superstructure for learning iterative algorithms Extend the superstructure to tackle AC-PF problems of different
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on strong cooperation between ICE-3, the University of Cologne and other partners within and outside Forschungszentrum Jülich, which provides an ideal fundament to combine competences across different
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these new methods at different sites and time periods, compare them with established approaches, and finally demonstrate their potential in a Europe-wide ecosystem reanalysis. The outcomes will include open