251 machine-learning "https:" "https:" "https:" positions at New York University in United States
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studies the neural mechanisms of fertility and mating behaviors in C. elegans and D. cerebrum. For more information about our work, visit: https://as.nyu.edu/faculty/emily-bayer.html This position is based
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of machine learning to the practical tools of deep learning, now available through modern foundation models. For the theory part, the selected candidate will work in close collaboration with
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Kawader webpage: https://nyuad.nyu.edu/en/about/careers/postdoctoral-and-research/kawader-research-assistantship-program.html For further information or questions regarding the position/program please
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, domestic violence victim status, ethnicity, familial status, gender and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing
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status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual orientation, unemployment
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processing, machine learning, and optimization theory. Strong verbal and written skills in English. Excellent analytical and problem-solving skills, and capacity to pursue independent research as
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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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Research Scientists as part of its new initiative, Polymathic AI, Building Foundation Models for Science. Recent advances in machine learning, including Large Language Models and diffusion based generative
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to teach the course 'Molecular and Cell Biology I' to undergraduate students during the NYU semester of August-November 2026. Class is tentatively scheduled to meet twice a week with lectures and
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, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive health decision making, sex, sexual