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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
: Algorithm Validation and Use Case Demonstration (Months 27–36): This WP will first develop an integrated hardware–software testbed to systematically validate the performance of proposed solutions under
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software development, and time management. The candidate will regularly visit the MetOffice for scientific exchanges and data acquisition. Useful recruitment links: For information relating to the research
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Communications plc, who will offer access to simulation tools, as well as technical and scientific support, thereby ensuring alignment with practical GNSS testing requirements. Please direct project specific
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Inc., France (https://www.inocess.com/?page_id=1370&lang=en ). The industrial partner will provide co-supervision, access to relevant data, offer opportunities for field testing of the proposed
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Inc., France (https://www.inocess.com/?page_id=1370&lang=en ). The industrial partner will provide co-supervision, access to relevant data, offer opportunities for field testing of the proposed
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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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regions, and may have also been observed in historical trends, but the processes driving this delay are not well understood. This project will use observations and climate model simulations to examine how
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pathogen screening, you will test two long-standing hypotheses: Fluctuating selection — Does spatial and temporal variation in pathogen pressure maintain diversity in immune-related genes? Antagonistic
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Methods Per- and polyfluoroalkyl substances (PFAS) are a class of over 14,000 synthetic “forever chemicals” that persist in the environment and the human body, where they are linked to cancers, immune
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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