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genetics to understand the complex relationships between severe mental illness, inflammation, and obesity and other adverse metabolic health outcomes. This research is important as people with severe mental
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healthy despite being at high genetic risk of dementia? This project will use cutting-edge genetic and molecular data from large human biobanks to identify molecular factors that help protect against
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localisation and sensing algorithms that achieve near-GNSS accuracy while improving robustness, energy efficiency, and spectrum utilisation in integrated terrestrial and non-terrestrial networks. Position You
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explore unconventional ideas, develop computer algorithms for data analysis, create new experimental approaches, and apply the technique in areas like biomedicine, materials science, and geology. My group
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and corrective feedback. You will apply advanced algorithms for machine learning, multimodal biosignal processing, and human-state inference, working with shared-control strategies and electrotactile
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of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning
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learning paradigms. The framework will support rapid prototyping, automated design-space exploration, and cross-technology benchmarking, providing new insights into the co-design of learning algorithms
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support. The PhD candidate will develop and validate a hybrid methodology that combines established stochastic optimization with AI-based learning. The aim is not only to develop new algorithms, but also to
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tuberculosis (TB) screening research, spanning the evaluation of novel, high-throughput molecular tests, innovative screening algorithms, and digital health tools. A core component of the role involves
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use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use