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of posterior curvature. The project will establish optimised OCT acquisition and de-warping methods, build robust segmentation and curvature-analysis algorithms, and validate clinically meaningful shape metrics
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and algorithmic framework to assess and improve the resilience of FL-enabled autonomous systems under such heterogeneity, explicitly incorporating the human in the loop. The project will draw
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the nonlinear dynamics of these networks using theory and numerical simulations. - Design and perform table-top robotic experiments that implement your learning algorithms in unpredictable environments. Who
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-robot testing. Work could involve new algorithms, reasoning pipelines, foundation model evaluation, benchmarks, platform integration and practical deployment limits. It suits candidates interested in
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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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cognition simulation algorithms, benchmark evaluations against direct behaviour-to-label baselines, interpretable markers of cognitive-affective dysfunction, and prototypes for non-invasive mental health
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sequence the complete DNA of babies at birth to screen for hundreds of genetic conditions. This PhD project will generate the evidence to inform which genes and variants should be included in newborn genome
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Mitochondrial diseases are among the most common inherited metabolic disorders. Despite advances in genetic diagnosis, important questions remain regarding the processes that drive disease progression and
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. The success of neural networks as a platform for developing AI is partially explained by the fact that backpropagation, the primary algorithm used to train them, runs efficiently on conventional CMOS hardware