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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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machine learning models to estimate individual numbers and distinguish species in complex field conditions. The resulting methods could later be applied to monitor waterfowl and scavengers in Lough Neagh
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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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that the modelling framework accurately represents real-world behaviour. This project offers an excellent opportunity for a motivated candidate to contribute to the global transition toward net-zero energy systems
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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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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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Efficient Quantum Architectures. arXiv preprint arXiv:2508.05339. Nugraha, F. P. and Shao, Q. (2023). Machine Learning-Based Predictive Modeling for Designing Transmon Superconducting Qubits, 2023 IEEE
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natural fibres and bio-resins, combining renewable materials with advanced processing and computer-aided design/simulation. The research aims to create high-performance, sustainable composites with tailored
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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