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Field
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the predictions of opaque models that are widely deployed in high-stakes decision making scenarios. Of particular interest to this project are example-based explanation methods that use individual data points
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methods for causal inference in observational data, is strongly preferred. Using various existing large datasets with rich information for knowledge synthetisation and triangulation over the course of the
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, manipulate large datasets, visualise data and perform numerical and statistical analysis is a requirement. Experience in handling 'big data', machine learning and working in distributed teams, is useful
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-field measurements, HPC-based numerical simulations, and laboratory experiments. Oldenburg is particularly renowned for its unique large wind tunnel with an active grid and leading expertise in developing
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-on experience with some combination of the following: Programming and conducting computational and statistical analyses on large administrative claims data, electronic health records-based databases, tumor
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The Nuffield Department of Population Health undertakes world-leading research into long term, chronic health conditions, operating large-scale long-term studies and projects to identify the causes
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-leading research into long term, chronic health conditions, operating large-scale long-term studies and projects to identify the causes, prevention and treatment of diseases that result in avoidable
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and curiosity-driven working culture. Research activities are carried out in research groups organised into three multidisciplinary research programmes. For further information please visit https
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Project 2: Experience working with large datasets and machine learning Reasonable proficiency in any coding language used in data science Mixed-methods research experience Online Application Required
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experience in pharmacy practice. Hands-on experience with some combination of the following: Programming and conducting computational and statistical analyses on large administrative claims data, electronic