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must hold a PhD degree or have submitted a PhD thesis. Responsibilities of the Position: Research Conduct world class research in systems security and privacy. Write research articles and publish work in
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computer science/engineering with expertise in a security or privacy research topic At least six years of relevant research experience in computer security, excluding the years spent obtaining a PhD or equivalent
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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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or quantitative methods. We are open to a range of methodological backgrounds, including digital trace data analysis, natural language processing, machine learning, experimental design, causal inference, and
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distributed decision-making. Applicants must have a PhD in Electrical Engineering, Mechanical Engineering, Computer Engineering, Applied Mathematics, Mathematics, or a closely related discipline, and are within
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and conferences. Candidates should demonstrate research leadership, the ability to develop and manage independent research projects, and expertise in machine learning and NLP. The successful candidate
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
. The candidate should have a PhD in Computer Science or a closely related field. Relevant background and skills include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval
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learning, Computer vision, and Autonomous Robots is desired. The successful applicant will work on various projects on robotics and computer vision, controls, and machine learning and their security
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learning and data analysis. Familiarity with one or more of the following areas: artificial intelligence, natural language processing, computer vision, multimodal learning, affective computing, human-robot
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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