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our mission we use a variety of research strategies that include big data and machine learning, brain imaging and neural interfacing, human-computer interaction and robotics. Bioinspired artificial
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for variational quantum circuit based functional regression. [5] M. Schuld et al., The effect of data encoding on the expressive power of variational quantum machine learning models. [6] A. Zeguendry et al
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, The seven tools of causal inference, with reflections on machine learning, Commun. ACM 62 (2019), 54-60. [3] A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. Müller, Causability and explainability
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These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG
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data for over 13,000 households in Great Britain. Energies, 14(21), 6934. Williams, S. (2020) Can machine learning be used to predict energy performance scores?. Available at: https
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years, machine learning (ML) methods are found to be effective in wireless communication, particularly for channel estimation in RIS integrated NOMA communication system. However, still required further
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-level fusion in combination with machine learning algorithms to achieve emotion recognition with some success. However, more recently deep learning has shown remarkable capabilities in learning detailed
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. A major focus will be on thorough evaluation of the method in multiple application areas including brain-computer interfaces. The successful PhD candidate will undertake their research
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an effective universal model to predict the risk of medicine non-adherence in patients with T2D. Machine learning algorithms will be applied to data collected in three different centres based in different
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Summary Deep learning is a hot topic in machine learning and it is playing increasingly important roles in intelligence systems for our daily lives, such as computer vision, autonomous car driving