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analysis of large-scale cohorts. The job requires strong skills in statistical inference and probability, and knowledge of designs and analytical methods for cohort studies. The candidate must have
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or public health research. Previous experience of applying statistical methods to epidemiological studies, including causal inference methodology, is desirable. You will possess excellent organisational
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the UKRI Future Leaders Fellowship “AI-Driven Inference for Gravitational Waves: Accelerating Discoveries in Fundamental Physics” (PI: S Green). We believe that talented and inclusive teams deliver
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of infectious diseases. • Experience with data analysis using statistical inference techniques. • Experience with health economic evaluations. • Experience with parallel and/or high-performance computing
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:………..………………………………………………………………………………………………………………………………………… Management, with particular focus on the intersection of: Machine Learning Causal Inference Applied Field Experimentation Scientific Areas: Management, Economics, Information Systems, Computer Science, Data
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model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis of large multidimensional real world data sets using Stata, R, Python
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, which has particular strengths in developing new statistical methodology, including methods for missing data and causal inference. The role will involve application and development of statistical theory
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the application of rock physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or
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of an undergraduate degree in a related field. Training in causal inference and/or machine learning methods Effective oral and written communication skills Outstanding academic credentials and intellectual creativity
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architecture design, simulations, and publicly available genomic datasets to develop new inference methods. The Postdoctoral Associate will conduct research related to creating or testing deep learning models