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. Application of artificial intelligence/machine learning to the big data from genetics and omics is well recognized in healthcare, however, its application to the data reported everyday as part of the clinical
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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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. Specific projects seeking applications are: Accelerating the discovery of inorganic solar-cell materials via a closed-loop, fully robotic synthesis–characterisation platform driven by multi-agent machine
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and research protocols in compliance-focused environments. Advanced computer skills with experience using Microsoft Word, Excel and PowerPoint; specific experience in working with a range of analytical
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Ability to work independently and collaboratively within an interdisciplinary team Excellent organisational, communication, and interpersonal skills Advanced computer skills, including Microsoft Office and
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hands-on technician with a strong technical foundation and the drive to learn. We’re looking for: Background in electrical, electronics, instrumentation, automotive, auto electrician or cabling trades
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and oral communication skills, including drafting of academic papers and grants High level computer skills with software skills such as Microsoft Office, SAS, SPSS If this sounds like you, we highly
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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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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