198 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" positions at New York University in United States
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, belief formation and revision, and the development of social cognition. Information about the lab’s research and recent publications are available at http://kidconcepts.org . Job duties include
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preferred Please submit a cover letter, resume, transcript, writing sample, and three references to Interfolio via https://apply.interfolio.com/182579. Applications are rolling with a final submission
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FAQ's please refer to our NYUAD Kawader webpage: https://nyuad.nyu.edu/en/about/careers/postdoctoral-and-research/kawader-research-assistantship-program.html For further information or questions regarding
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status, color, creed, disability, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status
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-quality clinical instruction and direct patient care while mentoring dental students in a supportive, hands-on learning environment. Key responsibilities include supervising students in clinical settings
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environment where machine learning meets real-world scientific impact. What You’ll Do: Conduct cutting-edge research at the intersection of AI and science Develop large-scale deep learning models for scientific
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Assistant Director, Learning Analytics US-NY-New York Job ID: 2026-15383 Type: NYU IT (WS1170) # of Openings: 1 Category: Technology New York University Overview The Assistant Director of Learning Analytics
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welcoming community that inspires its members to embrace and lead change in a rapidly transforming world. For more information about working at NYU please visit our website at: http://www.nyu.edu/about
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by 2040. Learn more at nyu.edu/nyugreen. NYU is an Equal Opportunity Employer and is committed to a policy of equal treatment and opportunity in every aspect of its recruitment and hiring process
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, but are not limited to, modeling high dimensional interacting systems in dynamic random environments, optimization, game theory and reinforcement learning, statistical methods, network science and data