66 parallel-computing-numerical-methods Postdoctoral positions at Yale University in postdoctoral
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Science Initiative has supported the rapid growth of the departments of Statistics & Data Science and Computer Science, as well as many interdisciplinary activities in which the postdocs could participate
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May 19, 2026 Deadline May 19, 2027 at 11:59 PM Eastern Time Description The Reilly Lab seeks a highly motivated Postdoctoral Associate to join an interdisciplinary research program focused
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neurodevelopmental disorders. The successful candidate will develop and apply experimental and computational approaches to understand gene regulation and disease mechanisms in neural development and to evaluate
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) Laboratory. The group develops clinically grounded artificial-intelligence and data-science methods to derive scalable digital biomarkers from echocardiography, cardiac computed tomography, electrocardiography
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pain under the mentorship of Dr. Nitya Bakshi, Associate Professor of Pediatrics and Director of the Pediatric SCD Program at Yale University. The research laboratory is focused on the study of chronic
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untreated patients in routine care. The postholder will be expected to design studies as target trial emulations with active comparators and new user designs where appropriate, use propensity score methods
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We are seeking a postdoctoral associate in intersectional feminist Science and Technology Studies. Preferred focus areas: critical computing/artificial intelligence studies; labor and automation
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Postdoctoral Associate | Mitochondrial Genomics | Lake Lab — Yale University, Department of Genetics
We are seeking a motivated postdoc to join our growing team in the Yale University Department of Genetics. Our lab uses experimental and computational methods to investigate cellular and health
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, Full-time (renewable) Position Description The research group of Dr. Xuehong Zhang at Yale University is seeking a highly motivated Postdoctoral Associate to join a multidisciplinary research program
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related psychiatric traits. We have work based in the uniquely-informative Million Veteran Program dataset, and also make use of many other datasets, including our own Yale-Penn sample, Thai sample, and