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of machine learning and advanced molecular dynamics techniques for molecular simulations and to study Nucleic acids structures and their interactions. For more information, please visit https://nyuad.nyu.edu
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mainly based in New York University Abu Dhabi, UAE, and can closely collaborate with researchers from New York University, USA. The ideal candidate will have an excellent knowledge of cyber security and
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September 1, 2026, subject to final budget approval and is available for up to three years. The post-doctoral associate will spend half their time on independent research, and half their time on collaborative
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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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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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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
proof checker, AI-assisted collaboration. Develop and analyze algorithms for learning and optimization. Participate in collaborative research projects and mentoring. Contribute to academic publications
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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learning, and federated learning. The Postdoctoral Researcher will be primarily based at NYU Abu Dhabi. The researcher will report directly to Dr. Farah Shamout and work in close collaboration with other
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM