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Field
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difference between new images captured by drone and the trusted datasets (the datasets that the deep learning model has been trained with and validated by an expert in the design time) to generate
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degree of oxidation. The scope of this project would be to design a methodology for capturing material property and gas transport data, gather data on these materials and subsequently build up
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makes this phenomenon compelling is not only its beauty in nature, but its potential to reshape the way we design novel artificial systems, like materials that can adapt to their environment or systems
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adapting their energy use, control strategies, and collective behaviours to enhance sustainability. The research aims to design AI-driven control and energy management frameworks that enable self-organising
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-designing a series of diverse people-centred futures with both experts and diverse citizens; iii) evaluating these visions with citizens and specialists; and iv) articulating the study’s contributions
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challenge that needs to be overcome. By understanding and controlling how small droplets dissolve and feed the growth of larger ones, we can design systems with finely tuned size, stability, and function
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shifts, and stringent latency demands render traditional beam management ineffective. This project will design, implement, and validate an AI-native predictive beam-steering framework that combines orbital
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to the design of aircraft, wind turbines and medical devices, and for modelling the environment. Remarkable advances in computing driven by the exponential miniaturisation of transistors (Moore’s law) have
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to interact with GE Vernova’s engineering staff and will have the opportunity to work, for part of the time, in GE Vernova’s main HVDC design office, based in Stafford, UK. Here the candidate will have the
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Geological Survey, Centre for Ecology and Hydrology, the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed