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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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—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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parcel delivery and environmental sensing. Equipped with diverse onboard sensors, including cameras and GPS, delivery UAVs hold significant potential for urban sensing applications such as infrastructure
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the University of Sheffield and with industry partners, you will develop and optimise the modelling techniques. This includes the development of Physics Informed Neural Networks and combine this with real-time
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microclimates that demand dense sensor networks and reliable data retrieval. This project focuses on developing nature-inspired hardware to deploy Internet of Things (IoT) sensors in forest ecosystems. Combining
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, durability, and environmental sustainability, while addressing cost constraints and net zero objectives. It will include an in-depth review of shortcomings in current design, based on literature review and