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Field
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Your Job: Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch
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experiments, and parametric investigations. The modelling component will be based, as much as possible, on semi-analytical thermo-mechanical formulations. This is to facilitate calculation accuracy, numerical
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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in
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targets the development of advanced coatings to prevent cell-to-cell propagation during runaway events. It combines experimental studies, numerical modelling, and real-world burner rig testing, culminating
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numerical simulation and analytical computation tools: Ansys HFSS, Matlab etc. • Strong interest in multidisciplinary, application-driven research with desire to innovate and pursue advanced research. •Highly
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of desired secondary structures. Experimental validation of the established AI model is also expected to be a part of this project. Specifically, your tasks will be: Running numerical simulations to generate
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use of HPC for numerical computations Good background in Wave physics Good oral and written presentation skills in Norwegian/Scandinavian Personal characteristics To complete a doctoral degree (PhD), it
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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
. Experimental validation of the established AI model is also expected to be a part of this project. Specifically, your tasks will be: Running numerical simulations to generate training data Building transformer
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moving monopoles (aeroacoustic sources). Among other factors, such a source model can be parameterized by the vertical wind speed profile at the turbine. The propagation model will rely on numerically