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(CFD) models, including fluid–structure interaction (FSI) simulations where appropriate, will be developed and validated against experimental measurements. These models will be used to investigate
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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activities with research tasks-related collaborators. About work tasks The research will involve theoretical works, the development of customized numerical scripts, and numerical simulations for validation and
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skills for programming and numerical simulations. Excellent written and oral English language skills. Meet the formal requirements for admission to USN’s doctoral program and The Faculty of Technology
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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to the theoretical development, numerical simulation, and potential hardware-mapping of these oscillator-based systems, exploring concepts like quantum annealing, symmetry breaking, and reservoir computing
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to the theoretical development, numerical simulation, and potential hardware-mapping of these oscillator-based systems, exploring concepts like quantum annealing, symmetry breaking, and reservoir computing
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machine learning (including deep learning and/or reinforcement learning) numerical simulation of quantum dynamics Proven programming skills (e.g., Python/Julia/C++), including experience with scientific
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the documents as stated in "How to apply". Excellent written and oral proficiency in English Applicants with backgrounds in computational mathematics, numerical analysis, applied and computational algebra
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/ open quantum systems quantum control / optimal control machine learning (including deep learning and/or reinforcement learning) numerical simulation of quantum dynamics Proven programming skills (e.g