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aspects of the project, including analyzing near-Sun observations of CMEs and shocks, running three-dimensional numerical simulations to model the Sun-to-planet evolution of CMEs and their sheath regions
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optimization of single-phase LCL filter inductors taking into account dynamic hysteresis models for different magnetic core materials. Supervisor: Prof. Paavo Rasilo (Electromechanics) Secondments: Université
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stability theory, modeling & identification, optimal control, certifiably safe & robust control, and learning for dynamics & control. The main task of the PhD student will be to develop sound data-driven
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to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model development using Python and/or other programming languages
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development. The successful candidate will contribute to the development of deep learning methods to predict reaction outcomes and optimal reaction conditions for organic reactions. The work will involve model