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Field
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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
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Delays to Sculpt Network Attractors. This is a brain inspired project on networks of oscillators to model patterns of functional connectivity seen in large scale brain recordings. These describe