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knowledge of statistical and computational research methods (e.g., predictive/user modeling), cognition/affect tracking, measurement theory, analyzing high-frequency time series data, experience with
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about the ILR School can be obtained at our web site, http://www.ilr.cornell.edu and CAROW at https://www.ilr.cornell.edu/carow . Requirements: Applicant must have a PhD in labor relations, sociology
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fields) or PhD in statistics, mathematics, or computer science (or related fields) with experience developing or using computational infectious disease models. Required Experience: Experience coding and
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national biodiversity indicators with international impacts. You will have advanced skills and knowledge of statistical analysis, research computing and Geographic Information Systems in relation
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environment, to assist trainees, and present their work in shared lab meetings and external meetings. Highly motivated individuals holding a PhD degree, preferably in virology, are encouraged to apply. The lab
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Fellow with a PhD in Computational Biology, Bioinformatics, Biostatistics, Data Science, or a related quantitative field to join an interdisciplinary research program focused on Alzheimer’s disease (AD
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• Design, plan, and execute experiments with the Principal Investigator • Coordinate ongoing research efforts and analyze/interpret results • Download, preprocess, postprocess, and statistically analyze
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the resulting data from the experiments. Required Qualifications: Candidate must have a strong quantitative background, with a PhD in computational biology, bioinformatics or related field including
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Qualifications: PhD (or equivalent) in Biostatistics, Epidemiology, or a closely-related quantitative field (by start date) Strong training in causal inference methods Experience working with large-scale
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Postdoc position in method development in human statistical genetics, with a focus on classificat...
University. Dr Speed's research involves developing statistical methods for better analysing data from genome-wide association studies (GWAS), with a particular focus on improving our understanding of human