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strong theoretical and numerical foundation in FEM, with applications in adaptive and performance-driven design. The work supports the broader goal of transforming how engineers and architects
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the gap between numerical simulations and clinical practice. The candidate will work alongside experts in solid mechanics, finite element analysis, and machine learning and cardiology, benefiting from
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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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short fiber thermoplastic composites Develop and validate numerical models and simulations to predict material and component performance Be prepared for changes to your work duties after employment