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. This unique position combines advanced finite element modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an
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modeling, machine learning, and experimental studies, while offering the opportunity to contribute to open-source libraries and collaborate directly with an innovative startup partner. You will be
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of surface sites makes theoretical understanding difficult. This project will develop and benchmark machine learning models to predict local electronic density of states (DOS) at alloy catalytic sites
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). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required
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of computational fluid dynamics (CFD). Knowledge of finite element method (FEM). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction
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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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to investigate flow-induced forces in hydraulic turbines under varying operational conditions and how these forces affect the degradation and lifetime of the machines. About the position The position is based
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introduces new and underexplored vulnerabilities to network-based threats. The goal of this research is to uncover such threats, evaluate their impact on training performance and model integrity, and develop
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We are seeking a highly motivated and skilled Postdoctoral researcher with interdisciplinary expertise to develop risk assessment and mitigation models using Large Language Models (LLMs
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and machine learning to tackle the complexity of force allocation and motion planning under uncertainty and actuator failures. The project combines theoretical research in stochastic optimal control