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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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to develop AI models for image reconstruction from data from our ultra-thin fibre-based spatial frequency domain imaging device (SFDI) and also from our custom-built photoplethysmography (PPG) sensor
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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mortalities, economic loss, and human health risks through contaminated seafood. A low-cost, deployable sensor network based on mussels could provide real-time environmental intelligence, supporting regulatory
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unsuitable, while the utility and performance of others remains an open question. The aim of the PhD is to derive synchronisation techniques suitable for deployment into a maritime radar sensor network
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authentication address digital risks, the physical interfaces of CPS (such as sensors and communication links) remain vulnerable and are often overlooked. A system cannot easily distinguish between genuine and
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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From safer surgery to sustainable factories and net-zero supply chains, we increasingly rely on robots to do work that is difficult, repectitive, or chronically understaffed. To be truly useful
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Distributed radar systems comprise a coherent network of spatially distributed sensors that can be independently transmitting, receiving, or both. By acting in unison, rather than in isolation