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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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of affective computing. The project aims to develop reproducible machine learning approaches for automatic emotion recognition in music, with stronger theoretical grounding, transparent model
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computing, including integrating AI with real-time energy data and carbon monitoring, contributing to low-impact, sustainable cloud operations. Distributed Federated Learning for Resource Management
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knowledge Distributed systems (mobile, edge and cloud computing) knowledge and programming skills Federated learning knowledge and skills Familiarity with processing and analysing sensory data Learn more about minimum entry requirements .
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impactful roles in AI, cloud computing, and large-scale system design. A practical example of this project includes, but is not limited to, distributed inference of foundation models across heterogeneous
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theoretical possibility, but may become a real threat in coming years due to significant advances in quantum computing technology. This project investigates the design, analysis and efficient implementation
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truck. Computing the location and time of these connections remains an unsolved computational challenge. This project will create new algorithms for calculating cost-effective and time-efficient routes
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truck. Computing the location and time of these connections remains an unsolved computational challenge. This project will create new algorithms for calculating cost-effective and time-efficient routes
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task-specific FL algorithms for NLP and Medical AI; 4) Design and implement a user-friendly FL framework for both research and technology adoption purposes. We expect to recruit a cohort of students
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) and data engineering for reproducible research. Familiarity with cloud/HPC workflows, containerisation (Docker), and basic bioinformatics pipelines will be preferred. Cross-disciplinary training and