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équipe.The requirements are the following: - A Master'sdegree in Applied Mathematics / Statistics / Machine Learning, - Advanced programming skills in Python and/or R. - Prior experience in biostatistics and
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The Department of Public Health at the University of Copenhagen offers 1 PhD fellowship in biostatistics with special focus on methods development and theory for causal inference. The anticipated
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, pharmacology/pharmacy, public health, epidemiology, statistics, biostatistics, machine learning/artificial intelligence, or social economics. Experience from relevant research projects will be considered
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, Health Economics, Epidemiology, Data Science, Biostatistics, or related disciplines, with post-doctoral research experience. They should have (i) a strong quantitative and qualitative background; (ii) a
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with leading European research partners. We are seeking a highly motivated candidate with a background in neuropsychology, medicine, neuroscience, molecular biology, biostatistics, pharmacology
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, Data Science, Biostatistics, or related disciplines, with post-doctoral research experience. They should have (i) a strong quantitative and qualitative background; (ii) a track record of publications
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motivated candidate with a background in neuropsychology, medicine, neuroscience, molecular biology, biostatistics, pharmacology, pharmacoepidemiology, public health, statistics or a related field. The
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and personal qualities The applicant must possess a Master`s degree or an equivalent in medicine, pharmacology/pharmacy, public health, epidemiology, statistics, biostatistics, machine learning
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background in biostatistics or data science, and a clear interest in applying these skills to questions of causal inference in longitudinal observational designs. Please note the funding is only available
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 12 days ago
have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using