23 postdoctoral-machine-learning Postdoctoral positions at Cornell University in postdoctoral
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Position Description The College of Arts & Sciences at Cornell University invites applications for a two-year postdoctoral fellowship (appointment title: Postdoctoral Associate) focused
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avenues of research rather than a required work plan. They may be pursued individually or in combination, and we welcome other creative and strategic approaches. Statistical or machine-learning approaches
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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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Freedom and Free Societies Postdoctoral Associate The Program on Freedom and Free Societies at Cornell University (https://freedomandfreesocieties.cornell.edu/ ) invites applications for a two-year
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. • Expertise in one or more of the following areas: statistical analysis of large data sets, machine learning, data visualization, and a high level of independence with a publication record to support these
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across learning sciences, computer science, machine learning, HCI and education research. Research Role Research themes for the NTO Postdoctoral Associate include, but are not limited to: Developing
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Postdoctoral Associate: Neurodegeneration – Hu Lab, Weill Institute (Research & Innovation) Postdoctoral Associate, Neurodegeneration – Hu Lab, Weill Institute The Weill Institute for Cell and
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are especially excited to hear from candidates eager to invent new tools, wetlab approaches for circulating nucleic acids, interpretable machine learning for biomarker discovery, and methods we haven’t imagined
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Rose Postdoctoral Associate-1 The College of Agriculture and Life Sciences (CALS) is a pioneer of purpose-driven science and Cornell University’s second largest college. We work across disciplines
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. The postdoctoral associate will be expected to work both collaboratively and independently on research projects, advancing computational methods using machine learning, developing automated pipelines for data