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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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of References Allowed Keywords statistics, biostatistics, computer science, economics, health care policy, causal inference, machine learning
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/guidelines . Minimum Number of References Required Maximum Number of References Allowed Keywords statistics, biostatistics, computer science, economics, health care policy, causal inference, machine learning
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for analysis (e.g., text manipulation); · One or more computational environments for statistical analysis (e.g., MATLAB, R, or Stata); · Creating and managing very large datasets; · Machine
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protein coding genetic association data with functional and machine learning-derived features 4. Developing methods to characterize the genetic architecture of autism Salary and Benefits This position is
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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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environments for statistical analysis (e.g., MATLAB, R, or Stata); · Creating and managing very large datasets; · Machine learning skills. Basic Qualifications A Ph.D. in any business discipline
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with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy researchers. The successful candidate will lead development of variable importance measures – including
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dynamic team. Information about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https://www.shih.hms.harvard.edu/ . What you’ll do: Design nucleic-acid nanostructures and