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. This project proposes the development of a new CFD simulator for offshore renewable energy applications based on physics-informed deep learning that offers greater efficiency and robustness. This is a unique and
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directly with speaker communities to ensure the technology is genuinely useful to them. You will gain deep expertise in machine learning and natural language processing, access to high-performance computing
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spectral analysis, entropy-based metrics, graph representations of cardiac conduction, and supervised, unsupervised, and deep learning approaches for classification of abnormal electrical activity within
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from the Finnish Meteorological Institute (FMI), as well as the DR will develop new and improved in-orbit E-sail experiments for future missions in lunar orbit and deep space. The DR will be enrolled
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. There is also a need for accounting noise in Deep Learning models and quantifying uncertainty in real-world applications. This PhD studentship (scholarship) leverages large-scale Earth Observation data
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research. The ideal candidate will have a strong foundation in Python programming and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. Applicants with a background in Natural
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ill-prepared to prevent, respond, or address deep, long-lasting impacts. In a world facing proliferating conflicts, the Leverhulme Centre for Research on Slavery in War (LCRSW) is the first overarching
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from flat model catalysts with advanced nanostructured electrodes engineered for real-world electrolyzers. This project combines deep dives into surface chemistry with engineering to produce a tangible
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will work with deep learning, affective computing, multimodal signal processing, graph neural networks, hypernetworks, temporal modelling and responsible AI. Expected outputs include personalised
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably