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of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication skills
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, CPRD) or hospital electronic health records Experience with data linkage and working with routine healthcare data Experience with machine learning or AI applications in healthcare settings Advisory
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genetic correlations, development and evaluation of polygenic scores, and integration of genomic predictors into multivariable and machine-learning prediction models for treatment outcome. Working closely
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a possible extension for one more year. The starting date is November or December 2025. This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological
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with data linkage and working with routine healthcare data Experience with machine learning or AI applications in healthcare settings Advisory or consultancy experience Understanding of implementation
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for spatiotemporal data (e.g., CNNs, LSTMs, Transformers, or Graph Neural Networks). Hybrid modeling: Experience with physics-informed machine learning or the integration of ML with data assimilation/multivariate
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designed to identify genetic and cognitive predictors of treatment response across large, well-characterised samples. The programme integrates genomic data with cognitive phenotyping, digital assessments
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the value of the data. Role Summary Design and conduct large-scale laboratory and field trials aimed at underground infrastructure Use classical geophysical sensors (e.g. GPR, gravimeter, magnetometer) as
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/drc/ ). About the role The role will contribute to on-going research at the UCL Hawkes Institute to develop advances in computational modelling of neurodegenerative disease, machine learning, and big
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. Expertise in artificial intelligence and machine learning. Recent research experience in the development of first-principle wave models. Recent research experience in the development of numerical codes