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The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes
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and multimodal applications. Required knowledge Candidates are expected to have a solid background in machine learning and Natural Language Processing. Research experience in multimodal research is
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models (eg auto-encoders and generative adversarial networks) and reinforcement/imitation learning algorithms for Markov Decision Processes. The application areas are different problems in text processing
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Machine learning is being used to make important decisions affecting people's lives, such as filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things
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, software architectures, Machine Learning
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one or more of the following areas: complex quantum processes, quantum error corrections, tensor networks, optimisation and machine learning, and developing software infrastructure Some experience in
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while inferring underlying physiological changes. Required knowledge Machine learning, dynamical systems theory, control theory, signal processing, time series analysis, neuroscience are all relevant and
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Machine learning, dynamical systems theory, control theory, signal processing, network theory, neuroscience are all relevant and a student should have strong knowledge in at least one of these and a
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gaming coaching reporting tools. The recruited PhD student will perform natural language processing and machine learning research into AI-augmented coaching to provide a pre-coaching session AI-based
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Many machine learning (ML) approaches have been applied to biomedical data but without substantial applications due to the poor interpretability of models. Although ML approaches have shown