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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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of fracture and high-cycle fatigue. The research methods are based on both small-scale and full-scale experimental testing and on Finite Element Modelling. Are you motivated to take a step towards a doctorate
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immediate leader will be associate Professor Victorien Prot. About the project This PhD position aims to increase our understanding of the mechanics of heart valve diseases using cutting-edge computational
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group conducts research on concrete from material to structural level based on theoretical, numerical and experimental investigations. The Concrete group is involved in several national and international
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exploring the use of advanced aerodynamic load models in dynamic analysis. The candidate will investigate the implications of applying mid-fidelity load models, such as vortex- or lifting line methods, in
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primary focus of the project, but complementary research on parallel systems may be developed. The project will add an important evolutionary component to ongoing interdisciplinary research on Arctic
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address this challenge using advanced experimental techniques, numerical simulations, and machine learning methods to develop high-fidelity 3D renderings of deformed samples during physical tests. By
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composites. You will combine experimental methods and computational modeling to push the boundaries of advanced manufacturing. Along the way, you will acquire expertise to build a foundation for a future
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your PhD project, topic, method, theoretical approach, and why this course will be relevant for your project. Maximum number of participants varies from 10 to 15. About the programme The programme has
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coordination of resources, including operators, equipment, and facilities, both within and across projects. This position will bridge this gap by exploring the application of logistics methods and digital