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networks that can be trained to do machine learning and AI tasks in a similar way to artificial neural networks. In this project you will develop machine learning theory that is consistent with the learning
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. They generally rely on expert rules or machine learning models to provide health advice. Recently, generative AI tools, such as ChatGPT, have become a popular focus of research. In healthcare, they show strong
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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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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
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, software architectures, Machine Learning
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the brain. This wouldn't be a typical machine learning PhD, as many aspects can only be examined on a philosophical and theoretical level. There may be scope to implement aspects in the ideas you develop
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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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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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models, such as ChatGPT and GPT4, incorporating the cutting-edge techniques in the other areas, such as reinforcement learning, causality and GFlowNets, to devise novel active learning algorithms for NLP
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species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species’ distribution shifts via “data