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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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person. Aims: The project aims to develop and evaluate AI methods for medical image analysis to detect diabetic retinopathy, glaucoma, cataract and age-related macular degeneration (AMD). As
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, encompassing advanced geospatial analysis, remote sensing methods, atmospheric transport modelling, and epidemiological data integration. The researcher will also receive guidance in handling large datasets
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networks, which could be used to support the transition to a sustainable, low-carbon energy future. The studentship will be awarded on the basis of merit. Students who pay international tuition fees
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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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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
generation of wireless communication (6G) to extend network coverage, supporting diverse data-intensive applications such as immersive extended reality and autonomous systems. However, aerial 6G networks will
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for decarbonising heat, industry, and district energy systems. This research takes a cross-disciplinary approach to explore the techno-economic, spatial, legal, and social dimensions of FPV-powered hydrogen in
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decision-making. Examples include crowd management and large-scale communication networks based on cellular or wireless sensors. For instance, during mass gatherings such as the sport matches (e.g
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impacts and suboptimal decision-making. Examples include crowd management and large-scale communication networks based on cellular or wireless sensors. For instance, during mass gatherings such as the sport
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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