191 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "U.S" research jobs at Harvard University
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research assistants (RAs); Machine learning skills; Writing papers for management and economics journals; Interest in reskilling initiatives; Working with partner organizations or companies. Basic
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What You’ll Need: PhD in computer science, artificial intelligence, machine learning, computational biology, biomedical engineering, or a closely related quantitative field. Strong foundation in modern
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nights and weekends Physical Requirements: Sitting using near vision use for reading and computer use for extended periods of time Lifting (approximately 20 to 30 pounds), bending, and other physical
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and/or testing of samples. May occasionally instruct others in basic laboratory techniques. Working Conditions: Work is performed on-site in Cambridge, MA. May be required to work with a variety of
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diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership
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, protected veteran status, disability, genetic information, military service, pregnancy and pregnancy-related conditions, or other protected status. Additional Qualifications Special Instructions Application
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postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health, including experimental design and reinforcement
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. Cross-Disciplinary Fellowships (CDF) are for applicants with a Ph.D. from outside the life sciences (e.g. in physics, chemistry, mathematics, engineering or computer sciences), who have not worked in
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may include—but are not limited to—AI-based grid operation and planning, reinforcement learning for distributed system coordination, electricity market design, pricing mechanisms for reliability and
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are desirable. We particularly encourage applicants with expertise in Multi-scale Modeling, Evolutionary Computation, Diffusion models, Reinforcement Learning. The successful candidate will work in a highly