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processes. Small oceanic flows on the 1-10 km range (submesoscale) have attracted lots of attention for their role in heat mixing, energy transfer, and air-sea interactions. They have been related to a
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. The student will incorporate the fast-evolving understanding of magma-mush systems into numerical models simulating surface deformation from porous fluid (magma) flow, and test how predicted subsurface stress
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, durability, and environmental sustainability, while addressing cost constraints and net zero objectives. It will include an in-depth review of shortcomings in current design, based on literature review and
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preliminary receiver positions. Despite these advances, current literature provides little guidance on how to systematically incorporate domain-specific constraints into the training and inference
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. Signal Conditioning: Embedding low-power electronics for amplifying and encoding the DA oxidation current into a transmittable signal. Wireless Data Transmission: Establishing reliable signal communication
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
such a promising technology, the centralised and resource-intensive nature of current LLMs conflicts with the constraints of aerial 6G networks in terms of limited computation, energy, and communication
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expand current technology to include automated live analysis, integrating machine learning algorithms capable of interpreting the complex behavioural patterns of mussels in response to environmental stress
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the onset period of monsoons is responding to climate change across different monsoon regions (e.g. Southern Africa, South America, Asia), and whether there is a common process linking monsoon onset changes
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detection. The successful candidate will engineer selective surface chemistries, advance flow-through detection methods, and apply the sensor to mechanistic studies of PFAS interactions with proteins and
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning