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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
operate in spectrum environments that are scarce, heterogeneous, and highly dynamic, which makes the traditional static and centralised spectrum management strategies inadequate for ensuring reliable, low
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computational fluid dynamics and numerical modelling will be used to simulate performance under varying runoff scenarios, pollution loads and climate conditions. By developing advanced road gully designs with
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membranes. These insights will inform both environmental monitoring and our understanding of PFAS toxicity at the molecular level. You will work within a multidisciplinary team led by Professor Vollmer
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), machine learning (ML), deep learning (DL) and Data science methods for medical image analysis, to autonomously grade the fundus images from large datasets. This will be supported by Professor Neil Vaughan
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will apply nonlinear and associational (colloquially called “causal”) timeseries analysis techniques to provide a more rigorous, and more statistically significant framework for understanding
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communities may have ecosystem level impacts that must be considered as part of sustainable management of the deep ocean, and in light of the new High Seas Treaty. This studentship will ask: how are deep-water
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about the system and its operational context. However, as environmental conditions evolve, these assumptions may no longer hold, leading to inaccurate predictions of adaptation impacts and suboptimal
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on their knowledge and assumptions about the system and its operational context. However, as environmental conditions evolve, these assumptions may no longer hold, leading to inaccurate predictions of adaptation
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About the ProjectProject details: Next-generation networks are rapidly outscaling the capabilities of traditional management paradigms. While early AI/ML models offered a degree of automation, they
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interdisciplinary expertise: biosciences to characterise the physiological responses of mussels under controlled exposures, and engineering to design hardware, firmware, and analytical pipelines that can run