9 big-data-and-machine-learning-phd Postdoctoral positions at Johns Hopkins University
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Professor Fei Lu and Bloomberg Distinguished Professor Mauro Maggioni on topics including mathematical foundations of data science and statistical/machine learning, with an emphasis on inverse problems and in
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of rigorous methodological approaches, including causal inference, quasi-experimental designs, and the use of large-scale claims data and electronic health records. The Fellow is expected to be highly
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- Research Internal Number: A-180117-11 General Description Salary: $62,232 - $62,232 a year The primary work for this fellowship will be in the area of bariatric interventions in a large animal model and
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experience managing large-scale surveys data. The two projects the post-doctoral associate will support are focused on consumer preferences for battery electric vehicle information and consumer preference
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sensitivity to QCD axions extending across nearly a decade of parameter space. The successful candidate is expected to participate in the operations and data analysis of the HAYSTAC experiment, as well as in
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Observatory for Rare Events (CUORE) and the CUORE Upgrade with Particle IDentification. Candidates should have a PhD in experimental nuclear or particle physics, or the equivalent. Preference will be given
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the Hopkins group is lead by David Kaplan and Surjeet Rajendran. The successful candidate will have expertise in theoretical particle physics and a PhD in physics or a related discipline by September 2026
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calibration using new surveys and multiple geometric anchors. • Participation in large collaborative and cross-calibration with additional cosmological involvement in the preparation and execution of new
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students. Applicants should have a PhD in physics prior to the start date and significant experimental research experience with atomic and optical systems. Applications should consist of a CV, list of