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discipline. This project would suit someone with: Strong background in wireless communication particularly 5G/6G/Satcom, terrestrial and non-terrestrial networks. Good Knowledge in aerospace system design, AI
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value. Yet these trade-offs remain poorly quantified in complex urban landscapes. This PhD will investigate how urban blue networks can be optimised for both ecological resilience and community wellbeing
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This PhD studentship covers fees and stipend for a home (UK) student to investigate how urban blue networks can be optimised to enhance ecological resilience and community wellbeing. The project
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components for real-time signal transmission. The design will include: Implantable antenna design: Developing a dual band antenna for efficient wireless communication and power transmission simultaneously
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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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including outreach to promote tech careers, networking with the TechFirst community and engagement with the tech industry. Qualification Type: PhD Location: Belfast Funding for: ROI students for QUB or UK
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both challenges and opportunities for achieving net‐zero carbon targets. While AI data centres are often perceived as passive electricity consumers, they could act as active participants in integrated
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PhD project: Modelling Resilience of Water Distribution Networks Supervised by Rasa Remenyte-Prescott (Faculty of Engineering) Aim: To develop an modelling approach for assessing water network
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students will be asked to take part in additional TechExpert activities for up to 10 days each year including outreach to promote tech careers, networking with the TechFirst community and engagement with
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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