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hands-on experience to turn your research into practical impact. Profile You are expected to have completed or will complete a Master’s degree in computer science, machine learning, statistics, physics
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of engagement. Read more at: Department of Computing Science Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven
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and machine learning with density functional theory, or other similarly relevant computational methods, to advance understanding of materials design predictions for 2D and 3D systems with electronic and
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Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating catalytic reaction mechanisms, with a focus
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machine learning. The ideal candidate would conduct highly collaborative dry lab research in areas of focus aligned with strengths in CAPS, the Department of Neuroscience, and the growing computational
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, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral, and interpersonal
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. Required qualifications include a PhD or equivalent degree with exceptional expertise in machine learning as well as postdoctoral qualifications and teaching experience equivalent to the requirements of a
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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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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