44 machine-learning-"https:"-"https:"-"https:"-"https:"-"RAEGE-Az" Fellowship positions at Harvard University
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Details Title Postdoctoral Fellowship Position in Visual Computing at Harvard University School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Sciences
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-of- origin–specific manner—and its critical roles in brain development, growth, behavior, and learning. The significance of imprinted genes is underscored by the diverse neural and behavioral
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with bioinformatic pipelines and approaches for working with methylation data, or a willingness/ability to learn these methods. The appointment is for one year with possibility of renewal based
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promote in our workplace culture. We work hard to ensure a healthy, inclusive, and positive environment where everyone does their best work in support of Harvard’s mission. Learn more about our commitment
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, operations research or related field. Additional Qualifications Candidates who have a strong mathematical background in reinforcement learning and/or control (e.g., optimal control, decentralized control, and
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, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status. Special
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the auto market, especially electric vehicles. The position will be under the supervision of Professor James Stock and Dr. Elaine Buckberg, and it will be housed in the multidisciplinary Salata Institute
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Harvard department of their choosing, but otherwise are expected to conduct independent research. They may not take courses for credit or teach more than one course per year, and they are expected to be in
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to continue with (and submit) your application). Learn More About OI’s Predoctoral Fellowship On Wednesday, January 21, 2026, at 1:00 pm ET, we will host a virtual information session (via Zoom) to provide more
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. in electrical engineering, applied mathematics, or related field. Candidates who have a strong mathematical background in reinforcement learning and/or control (e.g., optimal control, decentralized