692 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" "UCL" "UCL" positions at Harvard University
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assessed for impact. Responsibilities include: In the MDE Program: Co-teach the spring semester of MDE Studio course (STU 1232) annually. Studio is both a space as well as a pedagogical setting which can
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of and experience with quantitative methods (simulation modeling, optimization, and machine learning) preferred This is an annual term position reviewed each academic year on or before June 30th, with
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scientists, engineers, and/or doctors! The lab is committed to fostering lifelong learners in an environment that is diverse, inclusive and respectful. Learn more about our lab here: https
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, and either flow cytometry or microscopy, or both. The ideal candidate values patience, curiosity, and hypothesis-driven science, and is eager to learn new model systems and/or techniques. High standards
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caregivers Professional development opportunities including tuition assistance and reimbursement Commuter benefits, discounts and campus perks Learn more about these and additional benefits on our Benefits
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³ provides a new architecture for rigorous research, immersive learning, and cutting-edge practice. The Institute is built to dynamically adapt and scale, and responsibilities will evolve over time. Visit our
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, collaboration, and flexibility Enjoy our work, grow professionally, and aim for the extraordinary Learn more about Financial Administration (harvard.edu) and our eight reporting units. (https
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Skills Required: Strong computer skills and network-based applications. Demonstrated ability to operate the aforementioned systems, all of which reside on Operations Center Workstations. Additional
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a state in which Harvard is registered to do business (such as CT, MA, MD, ME, NH, NY, NJ, RI, and VT). Physical Requirements: Work will require sitting, near vision use for reading and computer use
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) Description: Apply Description Join us as a postdoctoral fellow in Professor Susan Murphy’s Statistical Reinforcement Learning Group. Our research concerns sequential decision making in digital health