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-cases of classical supercomputers, the development of quantum CFD algorithms will be of widespread benefit upon the arrival of fault-tolerant quantum computing. This project involves the adaptation
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) develop novel performance metrics combining accuracy and explainability, to be tested across different AI model types; (2) devise new algorithms for selecting models optimised for holistic performance
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creating robust, low cost, and real-time edge-AI algorithms capable of accurately classifying diverse marine species and debris under complex and dynamic underwater conditions. The demand for such a low-cost
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-terminal antennas and beamforming operating in FR1 bands and future FR-2, enabling robust terrestrial–satellite integration for safety-critical air mobility services. To develop AI-based algorithms
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Assessment Systems: Toward Trustworthy AI for Complex Educational Evaluation Image and Video Analysis Using Machine Learning Algorithms Mathematical and Computational Neuroscience, from neural data and network
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for Pollinator Monitoring: Train and optimise deep learning models for pollinator detection and classification using annotated image datasets. Post-processing object tracking algorithms will be incorporated