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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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new Wellcome-funded Imaging Machine learning And Genetics in Neurodevelopment (IMAGINE) lab, in the Research Department of Biomedical Computing. The post will benefit from the extensive and broad
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and resistance, and single cell RNA sequencing to characterise the resistant phenotype Apply mathematical frameworks to learn the dynamics of resistance evolution Combine experimental results with
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a working, learning and social environment in which the rights and dignity of all its staff and students and stakeholders are respected. We recognise the broad range of experiences that a diverse
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to compensate for such aberrations, significantly enhancing image quality. Adaptive requires knowledge of the wavefront to be corrected. Our team has been developing a machine-learning approach to wavefront
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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or computational modelling is desirable but not essential; applicants should demonstrate strong academic performance, resilience in the face of research challenges, a proactive attitude to learning and a willingness
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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teach you many translatable skills and knowledge from the fields of sleep medicine, sleep physiology, statistics, artificial intelligence, and psychology for example. A very significant and specific
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural