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, post-Bayesian methods, and in general computational and methodological challenges in integrative unsupervised learning. Method development will be motivated by the research questions of the funded
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applications in parametric modeling environments, including beam, shell, and solid-based elements. Creating a robust foundation in FEM theory and numerical methods, enabling the candidate to specialize in
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methods to be considered for numerical optimization by an Energy and Emission Management System (EEMS). Data-driven AI methods (e.g. Reinforcement Learning and/or Recurrent Neural Networks) to be considered
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identifying which patients will benefit from surgical valve repair. To address these issues, better patient selection methods and deeper insights into disease mechanisms are needed. This project proposes