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-mechanical phase-field model incorporating hydrogen diffusion, mechanical degradation, and fracture evolution. - Employ physics-informed neural networks (PINNs) to infer hidden fields and accelerate
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, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from nonlinear control and optimisation
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and
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generative modelling, and graph neural networks. Additional responsibilities include developing research objectives and proposals; presentations and publications; assisting with teaching; liaising and
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multispectral and/or SAR data to improve biomass recovery estimations, measuring biases between GEDI and EO time-series estimations, developing customised hybrid neural networks (e.g., CNN-LSTM for capturing both
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and prosthetic devices in the real-world. This PhD project offers the opportunity to work on pioneering research that combines state of the art computational modelling (deep neural networks) and
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cancer using graph neural networks. Our current efforts extend this to additional cancers and modalities, such as multiplexed immunohistochemistry (mIHC), immunoflouresence, spatial transcriptomics and
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investigating the neural and computational basis of anergia and effort hypersensitivity in depression. You will be responsible for: conducting behavioural, ambulatory smartphone-based and neuroimaging assessments
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in probabilistic AI. This initiative is motivated by the observation that many fundamental problems in AI could benefit from expertise in these disciplines. These include understanding deep neural