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PhD Scholarship Develop AI and machine learning models to guide real-time, personalised treatment of paediatric brain cancer using multiomics and clinical data, within the internationally
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motivation Testing whether the system performs equitably across cultural and language groups Training spans intervention design, trial methodology, human-computer interaction, and applied machine learning
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be used to combine these datasets while accounting for their different spatial scales, uncertainties and sampling frequencies. Machine-learning methods may also be explored for retrieval, bias
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. The research combines robotics, computer vision, artificial intelligence, machine learning, control systems, and medical robotics to solve one of the most challenging problems in modern automation. Project
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resulting model will support applications such as cognitive state estimation, brain–computer interfaces, and mental wellbeing monitoring. The learned representations will also enable integration with language
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, computer vision, federated learning, foundation models, adaptation techniques, multimodal learning, longitudinal image analysis or related areas, evidenced through coursework, research projects
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applicants with a background or experience in: • Computational chemistry • Materials simulation • Scientific machine learning / AI • Molecular dynamics or DFT • Materials science or a related discipline
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recognition Individual and group behaviour understanding Long-term behaviour prediction Multi-task learning and foundation models Zero-shot and open-set recognition Privacy-preserving AI using anonymised human
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with one of the following doctoral research projects. 1. AI-Aided Site Reconnaissance and Rapid Assessments for Settlement Upgrading How can spatial analytics, remote sensing, and machine learning models
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characterisation, to generate data-rich descriptions of evolving materials and processing pathways. A central aim is to couple these experiments with machine learning, mechanistic modelling, and automated data