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funding, including with writing technical portions of grant proposals. Required Qualifications* A doctoral degree in epidemiology, sociology, health behavior, health policy, statistics, or a related field
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-quality data visualizations to synthesize sequencing-based analysis results e.g.,Rshiny, d3, plotly, ggplot2. A solid understanding of statistics and experience in the implementation of machine learning and
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communication skills. The ideal candidate will use their computational skills, biological knowledge, understanding of statistics, and creativity to help us understand complex genomic data sets. Proficiency in
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projects in coordination with the PI with potential grant development and writing, as needed Required Qualifications* A PhD by August 2024 in survey methodology, statistics, biostatistics, public health
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science research institution in the world. This interdisciplinary community includes researchers from sociology, anthropology, economics, psychology, information and computer science, statistics, geography
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research, as evidenced by publications, training, and proficiency in statistical analysis. The fellow should have a career trajectory in the field of social epidemiology, cognitive aging, dementia, and/or
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factorization and other core signal processing concepts Expertise for data analysis and statistical evaluation inMatlab, C++, and Python High-performance computing skills Background Screening Michigan Medicine
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Gaynanova at [email protected] . Responsibilities* You will work with Dr. Gaynanova and her research team in developing statistical methods to analyze large-scale and high-dimensional biomedical data
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, computational and statistical genetic and bioinformatic methods implementation; vascular biology research using in vitro and in vivo models of cardiovascular diseases, particularly those that disproportionately
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to candidates with knowledge in survival analysis, machine/deep learning, image analysis, or high dimensional data analysis. Responsibilities* You will work with Dr. Li and his group to develop statistical models