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applicable, desirable criteria listed in the Candidate Information on our website. This post is available for 18 months. Fixed term contract posts are available for the stated period in the first instance but
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clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information. Fixed term contract posts are available for the stated period
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experience with management and strategy research, economic modelling and statistical analyses are essential. Familiarity with data mining, machine learning and computation techniques, especially in the context
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comparing model predictions of observed transient data Experience in software development with larger scientific codes; in particular, expertise in Fortran, C, Python, and/or build systems are of advantage As
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. Teaching and research experience in Health Economics / Econometrics are highly desirable. Experience in quantitative data and statistical analyses (preferably with health-economic data) with modelling
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, e.g. surveys, literature reviews, data gathering and/or recording of results using appropriate research methods. Participate in and/or present at conferences and/or workshops relevant to the project as
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, including other Harvard Medical School-affiliated hospitals. The qualified candidate will analyze multi-omics liquid biopsy data for minimal residual disease (MRD) detection, quantification, and assessment
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of Paediatrics at the University of Oxford. The goal of our research is to understand the information that shapes the complex physical architecture of the heart wall, and how it can be disrupted to produce
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nanoparticles and incorporation of cell-penetrable peptides for precise delivery to cancer cells, and exploiting differences in the membrane compositions of healthy cells and malignant cells. These data will be
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, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with an emphasis on maintaining physical consistency