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to protocol development, power/sample size considerations, and statistical analysis plans develop and apply advanced modelling approaches (e.g., survival and competing-risk models, causal inference methods
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nodes and chemical bonds as edges. Analysis these networks are important as they may provide AI-based approaches for drug discovery. This project will focus on representing and inferring chemical or
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to cloud-based machine learning services, on-device ML is privacy-friendly, of low latency, and can work offline. User data will remain at the mobile device for ML inference. Problems: In order to enable
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radio interferometry data, particularly very long baseline interferometry. Experience with or skills relevant to statistical modelling and Bayesian inference. Demonstrated familiarity with the fields of X
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–ligand interactions in plant–pathogen systems integrate diverse omics datasets to infer molecular interactions and regulatory processes design, implement and maintain reproducible, scalable computational
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been directly observed in planet forming discs around young stars (protoplanetary discs) and is inferred to be occurring around black hole discs. My research projects use a combination of 3D and 1D
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compressed into lightweight student models using knowledge distillation, enabling efficient real-time inference on mobile devices. The distilled models will be deployed and optimized on mobile platforms, with
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This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods
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that explicitly account for drift and open-set contamination. O2: Robust uncertainty estimation: Improve calibration and uncertainty reliability under drift (e.g., ensembles, Bayesian approximations, conformal
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This project focuses on brain network mechanisms underlying anaesthetic-induced loss of consciousness through the application of simultaneous EEG/MEG and neural inference and network analysis