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Field
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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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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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Sensor Technologies Research Group (STRG) to produce and validate screen-printed Ca2+ and Mg2+ electrodes using established protocols [3,4] and our in-house fabrication facilities for screen-printed
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pressure, not localized contractile forces. This project will develop a soft, capacitive iontronic sensor array integrated into a swallowable capsule to capture spatiotemporal pressure profiles of
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are urgently needed to monitor PFAS in water and probe their interactions with biological systems. This PhD project will develop a cutting-edge single-molecule optical sensor for real-time, ultra-sensitive PFAS
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to development and climate change. This project will analyse data from innovative motion sensors and a suite of other sensors deployed along the Alaknanda River, a tributary of the Ganges in India, since 2025
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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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of quantum sensors for acceleration sensing is a key priority due to its potential to revolutionise inertial navigation, environmental monitoring and geological surveying. Presently, the acceleration sensing