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Field
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capabilities in reasoning, planning, and generalisation, acquired through extensive pre-training, provide the foundation for a true cognitive engine capable of managing network complexity holistically
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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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frequency regulation, energy scheduling, and overall smart grid system optimization. Moreover, such complex interconnections between power system dynamics, communication networks, and information
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their applications. Using machine learning and related tools to enhance quantum memory advantages in stochastic simulation. Using advanced tensor network techniques to enhance the modelling of complex, memoryful open
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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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modelling framework to predict key thermal hydraulic parameters for boiling flows within complex geometries at high heat flux conditions, relevant to the engineering design of thermal management elements
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coordination capabilities essential for operations in complex urban environments. With advancements in 5G mobile networks, Artificial Intelligence (AI)-driven navigation, Internet of Things (IoT) connectivity
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Start Date: Between 1 August 2026 and 1 July 2027 Introduction: This PhD is aligned with an exciting new multi-centre research programme on parallel mesh generation for advancing cutting-edge high
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industrial SBSP experts, the candidate will explore the nonlinear structural dynamics of LSSs to fully understand the complexity of their control. They will use this foundation to explore idealised and