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postdoctoral position is available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research at the intersection of Geometry
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to compare the genetic architecture of rare vs. common genetic variation 2. Developing methods to analyze GWAS data using graphical models and genome-wide genealogies 3. Developing methods to integrate rare
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and statistics, and advanced programming skills are therefore desired. The role will also involve preparation of graphical displays of results and manuscripts for publication. Basic Qualifications Ph.D
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, Medicare and/or commercial claims data, a deep understanding of analytical methods and statistics, and advanced programming skills are therefore desired. The role will also involve preparation of graphical
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graphics, visualization, or computer vision. In addition to working closely with scientists, engineers, and graduate students on research projects, the candidate is expected to actively participate in