199 data-"https:" "https:" "https:" "https:" "https:" "https:" "Here We Are" Fellowship research jobs at Harvard University
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and contact information for three people who have agreed to serve as your reference Contact Information 10 Shattuck St. Boston, MA 02115 Contact Email heather_viana@hms.harvard.edu Salary Range https
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research and recent publications, see the Geometric Machine Learning Group’s website: https://weber.seas.harvard.edu/ . For questions, please email mweber@seas.harvard.edu Applications will be reviewed on a
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collaborative, impact-focused problem solver who wants to be part of a dynamic team. Information about the Shih Lab: Learn more about the innovative work led by Dr. William Shih here: https
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effect prediction. The fellow will work under the mentorship of Dr. Alex Luedtke and collaborate with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy
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: January 9, 2026 For more information, see http://warrencenter.fas.harvard.edu/. Basic Qualifications Applicants may not be degree candidates and should have a Ph.D. or equivalent. Fellows have library
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is research excellence and fit with the lab’s focus. More information on the lab’s research is available here . We especially encourage candidates with proven experience in applying computational and
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maintain a repository for offer letters, research plans, and other fellowship correspondence and documentation. Maintain list of all fellows’ key information, including dates of appointment, salary
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is research excellence and fit with the lab’s focus. More information on the lab’s research is available here . We especially encourage candidates with proven experience in applying computational and
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is research excellence and fit with the lab’s focus. More information on the lab’s research is available here . We especially encourage candidates with proven experience in applying computational and
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic