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computing. This will include, but is not limited to, the design of distributed quantum algorithms, circuits, and error correction, as well as the interplay between circuit optimization and circuit
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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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sets, optimal value functions etc. The broad goal is to build upon recent developments in learning Operator Theoretic representations of dynamical systems that focus on model interpretability
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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