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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
discovery, machine learning, and data science. The position will provide the opportunity to develop applied research skills in machine learning, interact with an international network of collaborators, and
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in AI-driven materials discovery, machine learning applications for materials, or generative AI related to materials. The successful candidate will independently lead a project focused on developing
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
the theoretical foundations of data science and AI. Responsibilities: Conduct research on automated reasoning and proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and
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. About LANDMark (Land Assets, National Development and Markets) This project, developed by Etienne Wasmer, professor of Economics and program head in Economics at NYUAD, and funded for the next 6 years
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to hire a post-doctoral researcher to work in any of the lab research areas, to be involved in the development of open source tools and resources, and to work on publications related to their work. CAMeL's
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to hire a post-doctoral researcher to work in any of the lab research areas, to be involved in the development of open source tools and resources, and to work on publications related to their work. CAMeL's
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expected to conduct high-quality, independent research leading to publications in leading peer-reviewed journals and conferences. Responsibilities include developing innovative research methodologies and
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theories, with the development of the mathematical constructions that are needed to provide such descriptions, and uncovering new geometric and topological structures arising from physical phenomena in high
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integrating environmental monitoring data, fisheries information, habitat datasets, and field observations to identify the ecological drivers of productive fishing grounds. Responsibilities include developing
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health