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(lola.adedokun@uky.edu). More information on benefits can be found here: https://postdoc.uky.edu/current-postdocs/ Skills / Knowledge / Abilities The candidate should have excellent written and oral
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citation record must be focused on AI; or alternatively (B), machine learning engineers with an AI-focused PhD and demonstrated 2-year industry experience in AI development Applicants must have in-depth
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comparative politics/public law, computational text analysis, and supervised machine learning/natural language processing on legal corpora. Postdoc 2: Lawmaking Institutions & Legislative Responsiveness Build a
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research areas especially along two themes: “Platform security and application”s and “Machine learning and security/privacy ”. Our goal is to provide the environment for the successful candidate to mature
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
for extension based on mutual interest. We are looking for individuals with a strong theoretical and practical background in large language models, machine learning, and natural language processing, combined with
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 hours ago
the group of Dr. Tengfei Li at the University of North Carolina at Chapel Hill. The successful candidate will develop and apply advanced statistical and machine learning methods for infant cognitive
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University Chicago, in partnership with members of the Learning Theory team at Google. More information about IDEAL can be found here: https://www.ideal-institute.org/ . The positions will be hosted by
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models that include these mechanisms. The postdoc will develop biologically-constrained machine learning–based model discovery pipelines to derive interpretable surrogate ODE/PDE models from simulated ABM
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these disciplines, gain access to top scientists and state-of-the-art equipment, and gain insight into research and career opportunities. You will have the opportunity to collaborate and learn from experts
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on the plants Arabidopsis thaliana will generate maps of depolarization, retardance, dichroism, and optical axis azimuth, which will feed machine learning models developed by the project partners to identify