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Field
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annotations are scarce or unreliable. Recently developed unsupervised learning methods allow to circumvent this limitation by learning patterns in unlabelled medical images and then leveraging them
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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tackling the above challenges with novel system designs and tailored AI/ML based methods. Candidate’s profile A First Class Bachelors degree and/or Masters degree in a relevant subject (computer
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compatibility with traditional composite matrices. Explore complementary computational fluid dynamics-discrete element method (CFD-DEM) simulations as a tool to predict fibre-fluid interactions and inform
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, developing solutions for removing and storing greenhouse gases into the built environment, and developing the life cycle assessment method for quantifying multiple cycles of using building products. Your role
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reversionary functions, will be formally defined and used in the architecture synthesis to allow assumption relaxation to be traded for increased performance. There is an opportunity to demonstrate your
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-generation defensive capabilities. The project focuses on moving beyond siloed detection methods to create a unified, multi-modal framework for identifying AI-generated threats. Its core aim is to develop a
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, covering all cardiac conditions. This makes them unsuitable for identifying rare or complex cases, where annotations are scarce or unreliable. Recently developed unsupervised learning methods allow
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multivariable statistical methods. Support for skills development is provided within the Horse Microbiome Research Group and the university’s Doctoral College . Delivery of this project in collaboration with