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established and emerging bioinformatics, statistical modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics
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, Isomap), manifold learning, and machine-learning classifiers to extract neural geometry metrics from both species. Systematically compare behavioural and neural data across mice and humans, identifying
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causal analytical foundations of mass transit system performance. The research is expected to contribute both novel methodological advances in statistical modelling, causal inference, and machine learning
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publications prior to the panel interview. In addition to further excelling your skills in Computer Vision/Big Data/Machine Learning analyses, this opportunity enables you to: - Work closely with clinicians
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of Dentistry, Oral & Craniofacial Sciences at King's College London. Our methodological work spans trustworthy AI, multimodal machine learning and statistical signal processing. Our translational work applies
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-scale, diverse datasets using machine learning and AI techniques. Findings will be disseminated through peer-reviewed publications, conference presentations, and public engagement. About You You will hold
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of an extension, subject to funding. You will apply and develop cutting-edge machine learning methods to integrate and analyse multi-omic data to identify disease phenotypes. A key aspect of the role is to bridge
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), with a start date from the 01st January 2027. You should hold a PhD in Computer Science, with expertise in Natural Language Processing, and have a demonstrated track record of research
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-scale, diverse datasets using machine learning and AI techniques. Findings will be disseminated through peer-reviewed publications, conference presentations, and public engagement. About You You will hold
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, Statistics, Computer Science or conjugate subject and have a strong record of publication in the relevant literature. Good knowledge of machine learning algorithms is essential, as well as proven competence in