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open-source tools and training modules for global utility adoption. The framework combines physics-informed graph-neural-networks (GNNs), diffusion model, and explainable reinforcement learning (XRL
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an exciting PhD scholarship to tackle one of the most pressing environmental challenges—detecting methane emissions from space using advanced neural network technology. This unique opportunity is part of
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at the intersection of causal learning, inference, and deep learning, leveraging Graph Neural Networks (GNNs) and Large Language Models (LLMs). The successful candidate will explore how GNNs can model causal structures
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for tomagraphic imaging in tissue Neural network correction of distortions in acoustic transducers web page For further details or alternative project arrangements, please contact: alexis.bishop@monash.edu.
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communications. Evaluation of model performance can be conducted based on the data collected through the water tank. We have the GPU machines ($14k) to develop deep neural networks for underwater communications