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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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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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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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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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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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to machine learning and deep neural networks, into the DG finite element solver to reduce computational costs while maintaining the accuracy. The key objective of this work will be to provide step-change
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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
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-informed data analytics tools for the predictive maintenance (PdM) strategy applications to high-value critical assets. Among others, the recently developed Physics-informed Neural Network (PINN) technique