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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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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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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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network engineering and angiogenesis 3). Applications of machine learning in cell and tissue engineering Candidates should have demonstrated publication records in cardiac and vascular engineering or
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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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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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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
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integrating them with microfluidics as a standalone device. This is a great opportunity to learn new skills, contribute to assay development, and intellectually contribute to projects within a collaborative
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contribute to overall lab operations. The applicant will be a collaborative, impact-focused problem solver who wants to be part of a dynamic team. Learn more about the innovative work led by Dr. Don Ingber
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manufacturing and maturation of cells and engineered tissues at low cost, and simple biofabrication technologies to build clinical-scale constructs. Learn more about the innovative work led by Dr. Chris Chen here