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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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                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 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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                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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                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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                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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                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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                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