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access to Justin Kerr's systematic photographic documentation of ancient Maya painted ceramics. The Postdoctoral Fellow in Pre-Columbian Art/Archaeology will receive training in archival processing and
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 06-Mar-26 Location: Cambridge, Massachusetts Categories: Academic/Faculty Computer/Information Sciences Internal
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) are for applicants with a Ph.D. in a biological discipline, who will broaden their expertise by proposing a project in the life sciences which is significantly different from their previous Ph.D. or postdoctoral work
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, neuroprosthetics or neuromodulators, peripheral nerve surgery, neural interfacing models Track record of disseminating research through impactful publications and presentations (at least 3 first author publications
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) datasets, including the Nurses Health Study cohorts, and the Health Professionals Follow-up Study. Data/Statistical Analysis. Processes, organizes and summarizes data, reporting results using a variety of
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for Biomedical Imaging (Harvard/MIT/Mass General). In parallel, there will be opportunities to analyze and publish existing data upon identifying areas of mutual interest. The appointment is for one year with a
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. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard's academic purposes. Harvard has an equal
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Match for Similar Jobs About Harvard University Harvard University is devoted to excellence in teaching, learning, and research, and to developing leaders in many disciplines who make a difference
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at the time of application are not eligible. Applicants must complete postdoctoral training in a lab different from that of their graduate (thesis) lab. Deadline Month: October Description: The Foundation
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decarbonization, grid modernization, and the integration of distributed and flexible energy resources. Research topics may include—but are not limited to—AI-based grid operation and planning, reinforcement learning