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Field
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machine learning challenges: how to learn from limited and heterogeneous data, how to combine data with physics-based models, how to solve inverse problems under uncertainty, and how to build models
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approaches. The research will focus on fundamental thermomechanical and thermofluid phenomena relevant to fusion devices and components. In particular, it will investigate uncertainties arising from
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is to obtain imaging methods that are more robust and computationally efficient, while also providing a natural framework for uncertainty quantification and experimental design. A central question is
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be used to combine these datasets while accounting for their different spatial scales, uncertainties and sampling frequencies. Machine-learning methods may also be explored for retrieval, bias
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becoming increasingly important as the energy system faces more uncertainty from weather, renewable generation, market developments, and future electricity demand. These decisions depend on uncertain inflows
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a major scientific and societal challenge. It requires fundamentally new approaches to control and optimization in environments where multiple agents interact under uncertainty and constraints. In
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will be used to improve and calibrate wind farm models, quantify their uncertainties, and experimentally assess the performance of Helix and potentially other wake-mixing and wind farm control strategies
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-time operation. The project will focus on how AI and mathematical optimization can be combined to support sequential bidding decisions under uncertainty. The initial use case will consider a wind power
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, incomplete, inconsistent, or affected by uncertainties, limiting their use for asset knowledge, infrastructure maintenance, and urban risk management. In this context, the central research question of this PhD
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. Accurate prediction of water inflows remains a significant challenge, and uncertainty can increase the risk of seepage, flooding, operational delays, and additional costs. To mitigate these risks, dewatering