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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
such a promising technology, the centralised and resource-intensive nature of current LLMs conflicts with the constraints of aerial 6G networks in terms of limited computation, energy, and communication
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relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and
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: • Experience with programming (Python, MATLAB), • background in aerospace, computer science, robotics, or electrical engineering graduates, • hands on skills in implementation of fusion
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level (or international equivalent) in a Science, Mathematics or Engineering discipline. Applicants without a master's qualification may be considered on an exceptional basis, provided they hold a first
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Zero commitments, generation from renewables will form a key part of the electrical energy generation mix. Much of this generation will be located offshore and will need to be transported to shore using
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. Entry requirements: Applicants will normally need to hold, or expect to gain, at least a 2:1 degree (or equivalent) in Mechanical Engineering, Electrical Engineering, Mechatronics and Control Engineering
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, people who identify as an ethnic minority, people with disabilities and people from LGBTQ+ communities, as they are currently under-represented in the Department of Electrical Engineering and Electronics
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alternative, offering electronically controlled release at designated GI sites. While ingestible devices demonstrate the promise of electronic capsules, they also highlight current limitations. Using relatively
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-institutes/research-in-electronic-electrical-and-systems-engineering/communications-and-sensing/communications-electromagnetics ) and Metamaterials and Nanophotonics group (https://www.birmingham.ac.uk
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strategic priorities in Digital Twins, Environmental Intelligence, and Data-Driven Engineering, using advanced computational modelling to support ecosystem resilience and sustainable management. The project’s