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models such as Random Forest and Neural Networks to help understand and predict pairwise interactions between pollinators and plant species. - Software Engineering: integrate models into a standalone
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engineering, computational neuroscience, artificial neural networks and bio-inspired robotics: "Rhythmic-reactive regulation for robotic locomotion" (Supervisor: Prof Fulvio Forni) will apply techniques from
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interest in expanding their knowledge in both domains. (1) Geometry/Topology -related methods in computer science. (2) Machine Learning. (For example, graph neural networks, generative networks, or neural
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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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-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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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling