23 postdoc-in-thermal-network-of-the-physical-building PhD positions at University of Exeter
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an important role in the efficient integration and management of solar energy in modern power systems. The studentship project aims to develop a novel PV forecasting model based on physics-informed neural
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space that the LLM can natively process. Second, a task-specific networking head will replace the LLM’s default language modelling head. This module will map the LLM's output features directly to a
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conditions. For instance, cameras used to monitor the behaviour of nesting birds typically require mains power or are too bulky to fit unobtrusively in nests without disturbing the occupants. Thermal imaging
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
representations and embedding techniques that allow LLMs to natively interpret spectrum-related information, capturing the unique temporal, spatial, and physical characteristics of 6G signals. Building
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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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biosensor, transforming an existing prototype technology into a deployable environmental monitoring solution. Building on recent innovation at Exeter, this PhD will translate a novel discrete gape-sensor unit
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Methods Reaching Net Zero is one of the greatest scientific and societal challenges of our time. Reducing emissions is essential—but unfortunately not by itself enough. To stabilise the climate, we must
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this project have direct applicability to UK commercial forestry and Net Zero ambitions due to increasing tree planting programs coupled with increased extreme weather events. Please direct project specific
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expertise in perturbed parameter ensembles and machine-learning model integration. A CASE placement at the Met Office will further strengthen your applied training and professional network. CASE Partners