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and machine learning methodology to help deal with key challenges in developing such models in large-scale observational electronic healthcare record data. These models will be applied to important real
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The continuous release of Earth Observation (satellite) data and the emergence of Machine Learning methods open up new possibilities for understanding forests. These large datasets provide
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on clinical complications, and use machine learning to develop and validate predictive models to identify high-risk patients. The research aims to individualise inpatient care, reduce hospital-acquired
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monitoring. Candidates should have a background in computer science, AI, machine learning, affective computing, computational psychology or related areas. Strong programming skills are essential. Funding
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than over a set period as with the main exam periods. There are three types of invigilation: - Standard main hall - Specific provision (students with specific requirements) - Online/computer based. You
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/Artificial Intelligence and their data, modelling, and operational challenges. Develop and evaluate algorithmic solutions (e.g., machine learning, optimisation, statistical modelling, distributed systems
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. Develop and evaluate algorithmic solutions (e.g., machine learning, optimisation, statistical modelling, distributed systems), depending on project direction. Implement research prototypes and run