168 parallel-and-distributed-computing-"U.S" Fellowship positions at Harvard University
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Postdoctoral Research Fellow position in statistics, genetics, and biomedical AI. The lab develops cutting-edge theories, methods, and computational tools for integrating large-scale, heterogeneous biomedical
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and professional development funding will be available. The aim of this program is to expand the pool of talented academic leaders equipped to conduct research that advances health equity using
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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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Director of LISH (Dr. Ramona Pop). The position involves conducting rigorous empirical research using field experiments, large-scale data analysis, and computational methods to advance our understanding
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Fellowship program offers comprehensive research, training and professional development opportunities for scholars advancing their careers in history. Fellows pursue their research with support from
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computer science, statistics, operations research, or related computational fields. As part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel
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design (using computer-aided software). Special Instructions A cover letter and current CV are required as part of the application. SEAS is dedicated to building a diverse and welcoming community, and we
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(D^3) Institute and the LISH/Data Science & AI Operations Lab seek enthusiastic Postdoctoral Fellows skilled in computer science, statistics, operations research, or related computational fields. As
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the Senior Director of Labs (Dr. Ramona Pop). The position involves conducting rigorous empirical research using field experiments, large-scale data analysis, and computational methods to advance our
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transform visual information. We use large-scale electrophysiology and closed-loop visual experiments with macaque monkeys to discover the principles underlying cortical computation. We are seeking highly