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Machine Learning. Candidates must have an MSc at distinction level (or equivalent) in applied mathematics, numerical analysis or similar and be eligible for UK fee status. Candidates should have a solid
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. Computer programming in R, Python or STATA English Language requirements: IELTS 6.5 overall (minimum of 5.5 in all other sub-skills) Desirable: Masters in AI or Machine learning would be advantageous How
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Funding providers: Swansea University's Faculty of Science and Engineering Subject areas: Computer Science (Machine Learning/Pattern Recognition applied to molecular cardiology) Project start date
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Uncertainty quantification for machine learning models of chemical reactivity In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions
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View All Vacancies Chemistry Location: UK Other Closing Date: Sunday 12 May 2024 Reference: SCI266 Uncertainty quantification for machine learning models of chemical reactivity In this PhD
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screening and process optimisation platforms has the potential to significantly streamline this process through machine learning guided experimentation. This interdisciplinary project, based across
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is expanding its PhD research in the area of medical data analysis. We aim to implement machine learning to analyse atomic force microscopy nanoindentation data towards automated diagnosis of cancer
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This 4 year PhD project is funded by EPSRC (funding from Materials 4.0 CDT and Rolls-Royce Submarines). The funding is for UK students only; tuition fees will be covered and you will received a
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Cardiomyocyte Dynamic Network Analysis with Machine Learning (Ecidna-ML) This project represents a new approach to map dynamical interactions in networks of human cardiac cells. Network dyssynchronisation is a
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Engineering. In addition to the research project outlined below you will receive substantial training in scientific, technical, and commercial skills. This project looks at efficient quantum machine learning