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as “range anxiety” are amongst the top barriers to the wide adoption of EVs. This project aims to develop a paradigm for data-driven modelling of EV behavours, optimisation for EV charging station
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biological networks as a form of relational and structural learning. Given a network dataset, we wish to infer a model of the distribution of the elements of this data-set, possibly as a mixture of several
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. I clear example would be teams of nurses training in simulated scenarios. Sensors such as positioning trackers, physiological wristbands, microphones and eye trackers could be used to model complex
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techniques for annotation, active learning (based on either deep learning or Bayesian learning), semi-supervised learning, transfer learning, imitation learning, etc., aiming to ensure the data and models