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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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learning in general. OCBE has numerous collaborations with leading biomedical research groups in Norway and abroad. This therefore is a unique opportunity to contribute to cutting-edge research in statistics