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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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in exercise and dance interventions. Build and evaluate AI / machine learning models using labelled, collected multimodal data to classify motor and non-motor symptoms, identify digital biomarkers
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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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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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computational fluid dynamics (CFD) and computer-aided design (CAD) software. They should also be prepared to engage in both computational analysis and experimental testing as required. Essential criteria
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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