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quantitative genetics, Bayesian methods, machine learning, large-scale genomic datasets, single-cell omics or integrative omics analyses would be highly regarded if the candidate was not initially trained in
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for the AiTASHA project. We welcome applications from candidates who: - Have research expertise in statistics with a strong mathematical foundation. - Have experience in AI, machine learning or deep learning, and
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research projects involving large administrative claims, EHR, survey, or clinical trial data. Collaborate with external collaborators in implementing machine learning algorithms. Perform other duties as
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex network models to partially observed hospital infection data, leveraging patient movement data. This research will
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methods in meteorology (atmospheric data assimilation). This contributes to the cutting-edge research expertise of the College and the University in the strategic areas of Machine Learning, Statistical Data
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in the areas of statistical methods for high-dimensional and/or large-scale data, mathematical and statistical foundations of machine learning, statistics of stochastic processes and dependent data
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recordings. More information on the smartphone can be found here: https://www.kneuhealth.com. The successful applicant will work closely with our senior postdoctoral scientist on Deep learning and Machine
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Assistant Professor in statistics for the development of privacy-enhancing techniques in health care
following areas: Synthetic data generation Machine learning Large health register data GDPR compliance rules Valued personal competencies include being independent and creative, having an outgoing personality
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expertise in statistical computing and/or in the interaction between statistics and machine learning/artificial intelligence. The appointment comes at an especially exciting time for the school, as we expand
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independent research, develop original ideas, and apply advanced quantitative or computational methods. Experience in statistical modeling, machine learning, experimental design, data analysis, or computational