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Field
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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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-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
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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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skills in one or more languages (Python, C/C++, or others) experience in mechanical testing profound knowledge of machine learning methods (e.g., neural networks, Gaussian processes, active learning
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identification of biological sounds using passive acoustic data. Passive acoustic monitoring will be conducted with species identification based on a neural network trained and tuned to the turbulent waters
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Appropriate computational skills and knowledge of programming languages (Python, C++, etc.) Experience with Machine and Deep Learning models and software (Keras, Scikit-Learn, Convolutional Neural Networks, etc
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. The School comprises of four Research Groups, which are: Artificial Intelligence Brain Computer Interfaces and Neural Engineering Communications and Networks Robotics and Embedded Systems Research within
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. This project will rely on recent advances in neural networks to develop machine learning potentials (MLPs) for MD simulations of realistic nanomaterial/coolant-liquids and use these to gain fundamental insights
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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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learning approaches and develop a theoretical understanding potentially based on differential geometry. In particular, deep neural networks perform surprisingly well on unseen data, a phenomenon known as