186 parallel-and-distributed-computing-"DIFFER" Fellowship positions at Harvard University
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experiments, large-scale data analysis, and computational methods to advance our understanding of innovation and digital transformation. Candidate Profiles: We seek candidates with strong quantitative and
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Public Health Academic Pipeline Program in Maternal and Child Health (MCH) of the Harvard T.H. Chan School of Public Health (HSPH) invites applications for a full-time postdoctoral training fellowship
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might be shared between insurers, governments, and households. Candidates should have some familiarity with some or all of the following: · Causal analysis and experimental design: difference-in
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to the Kavli-Laukien Postdoctoral Fellowship at the Origins of Life Initiative at Harvard University. The Kavli-Laukien Fellowship program supports young developing independent scholars to pursue innovative
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phenotypes. The lab uses a variety of experimental (functional genomic, targeted genetic) and computational (bioinformatics) tools on human and mouse tissues and using in vitro methods on human cells
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place across the departments of Physics, Chemistry and Chemical Biology, Mathematics and the School of Engineering and Applied Sciences. Active research areas include quantum information and computer
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to develop an innovative research program in sequential decision making. Our lab is involved in digital health studies in dental health, cardiac health, physical activity, mental illness and substance abuse
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assessment, and ambulatory behavioral assessments to precisely track brain and cognitive change over short intervals. The program of research seeks to understand individual differences in aging trajectories
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research focused on well-being and public health supervision or mentorship experience demonstrated interest or experience in program development and community engagement preference for previous experience
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with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy researchers. The successful candidate will lead development of variable importance measures – including