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science, artificial intelligence, machine learning, computational social science, data science, or a related computational discipline. Applicants must have experience with digital trace/multimedia datasets and in
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. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely related field) with significant post-qualification research experience. You will experience
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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development About You You will hold a Ph.D/D.Phil in a quantitative or theoretical discipline (e.g. machine learning, computer science, mathematics, statistics, physics, theoretical neuroscience or a closely
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, clinical and sensor data processing, quantitative image analysis and machine learning. They will establish and maintain robust research databases and FAIR-compliant data management processes for large, multi
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multidisciplinary unit, with expertise in mathematical modelling and machine learning, wet-lab expertise in multiplex serological and genetic assays, expertise in diagnostic development and production, and expertise
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matching, optimal transport or cell-cycle modelling. Key Responsibilities These include but are not limited to: Leading an independent research project in scientific machine learning and mechanistic
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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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others, gained through research, academic, or applied settings Experience applying machine learning, natural language processing, or other computational methods to health, biomedical, or research problems
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bioinformatics analysis of omics datasets, including single-cell and spatial multi-omics analysis. You will have demonstrable experience of applying machine learning/AI methods as well as experience and knowledge