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supporting the Net Zero 2050 target. This PhD project will develop an AI-enabled framework that optimizes wind turbine control and predictive maintenance. Using Deep Reinforcement Learning (DRL), the system
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candidate will contribute to feasibility studies on hybrid propulsion systems, the development of robust control architectures for autonomous docking, and predictive maintenance strategies using real-time
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recycle content crush alloys. The main objective of the project is to understand the deformation behaviour of the high recycle content crush alloys and the role of tramp elements in controlling the final
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sources, and that the operations must be less predictable in their chosen transport means and locations. Thus, the goal is to explore reduced predictability while sustaining the units to be supplied
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load predictions for wind turbines, specifically the foundations, with the ultimate objective of including structural health information in windfarm asset management to optimise structural lifetime
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) therapy on the biology of γδ T cells and how can we use this knowledge to help us predict the success of therapy and prevent the development of side-effects. Position 1 will focus on the cellular and
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for optimizing metals microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM
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measured data, apply necessary filtering and selection of data features to be stored. Couple the numerical model and the measured input data to establish a model that can predict the outcome in terms
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restraint conditions. A key goal is to develop both a sensor system and a prediction model for the short- and long-term deformation behaviour of concrete. These tools will be applied to full-scale structural
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“Quantitative predictions of protein – DNA interactions from high-throughput biophysical binding data”. Sequence specific binding and recognition between transcription factors and DNA control gene expression at