74 phd-in-computer-vision-and-machine-learning Postdoctoral positions at Duke University
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will receive full support for making connections with other scholars and joining campus intellectual life, as well as mentorship from GSF faculty. Applicants should have PhD in hand by July 1, 2026, and
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, Astrophysics, Computer Science or a related field by the start date ● Strong background in observational or computational cosmology, large-scale structure, weak lensing or image processing ● Proven experience in
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and mathematical modeling, hierarchical statistical modeling, machine learning, remote sensing, geospatial statistics) • Demonstrated ability to conduct independent research and publish high-quality
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. Department of Pharmacology & Cancer Biology is looking for a Postdoctoral Associate to work alongside Dr. Chris Chidley in research projects. Minimum Requirements: PhD in Pharmacology, Molecular Biology, or
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Appointee holds a PhD or equivalent doctorate (e.g. ScD, MD, DVM). Candidates with non-US degrees may be required to provide proof of degree equivalency. 1. A candidate may also be appointed to a postdoctoral
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the lab; publish peer reviewed manuscripts and contribute to funding proposals. Educational Requirements • PhD in Chemistry, Bioinformatics, Computational Biology, Computer Science, or a related field
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trial design and conduct, and scientific writing. Qualification: PhD in nutrition, epidemiology, biostatistics or related field. Strong quantitative and analytical skills, including experience with
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require self-direction and the ability to work effectively with other team members, undergraduate, graduate, and other post-doctoral researchers in the in lab. Candidates must have obtained their PhD in a
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multiple affiliations. Postdoctoral FellowPosition Computational approaches to malaria parasite antigen diversity Duke Global Health Institute Be You. The Malaria Collaboratory is recruiting an exceptional
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data, identifying structural errors in the dataset, and for maintaining a record of all steps from data extraction to dataset assembly · Fitting of machine learning models · Development of instrumental