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, biodiversity monitoring, and climate resilience. The work supports strategic priorities in Environmental Sciences, Software/Cyber. PhD researchers will explore how AI-driven Earth observation, computer vision
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, G. et al. Machine learning and wearable sensors for automated Parkinson’s disease diagnosis aid: a systematic review. J Neurol 271, 6452–6470 (2024). https://doi.org/10.1007/s00415-024-12611-x Nayan
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and machine learning techniques for the design of superconducting qubits, one of the leading qubit modalities used in today’s quantum computers. The optimal design of superconducting qubits is a highly
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-based transfer learning classification model for two-class motor imagery brain-computer interface. International Journal of Neural Systems (IJNS). https://doi.org/10.1142/S0129065719500254 * Kudithipudi
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Summary Meeting future energy demands requires efficient, low-carbon systems capable of storing and releasing heat when needed. This PhD project aims to develop next-generation latent heat thermal
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Apply and key information This project is funded by: Department for the Economy (DfE) Summary This PhD project offers an exciting opportunity to develop next-generation biocomposites made from
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approaches often provide only limited insight into these effects. This project will use advanced computer simulation, informed by post-operative scans and patient movement data, to understand how variations in
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, Jose Landivar-Scott, Nick Duffield, Kevin Nowka, Jinha Jung, Anjin Chang, Kiju Lee, Lei Zhao, Mahendra Bhandari, Unmanned aerial system and machine learning driven Digital-Twin framework for in-season
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., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1833. Springer, Cham. https://doi.org/10.1007/978-3-031-35992-7_2 Familoni
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mobile health technologies enable the continuous capture of rich, multimodal physiological and behavioural data. These data when analysed with Artificial Intelligence (AI) and machine learning methods can