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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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Supervisor(s) 1) Professor James Gilbert, University of Hull 2) Dr Hatice Sas, University of Sheffield Increasing productivity and yield in the manufacture of wind turbine blades is a key priority for the UK offshore wind sector, as set out in the Offshore Wind Industrial Growth...
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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
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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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research group, which leads pioneering work in multi-sensor navigation, signal processing, and system integrity for aerospace, defence, and autonomous systems. The research will deliver a comprehensive
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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