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at the intersection of AI and any humanities or social science discipline. Fellows will have the option to teach one undergraduate course each year. The first year of appointment will run 1 August 2027–31 July 2028
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. For example, investigating costs and benefits related to the adoption of state-of-the-art technologies that permit tracking and chain of custody. The team will also explore machine learning and artificial
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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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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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mechanics, machine learning or applied/computational mathematics. ● Demonstrated ability to carry out original mathematical derivations and/or develop computational tools, evidenced by publications
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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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machine learning, Computer vision, Swarms, Autonomous Robots, hardware security, and Embedded systems development is desired. The successful applicant will work on various projects on robotics and computer
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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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analysis. ● Prior work on kinase or other signaling-protein conformational dynamics, phosphorylation-driven activation, or allosteric regulation. ● Familiarity with machine learning and deep learning
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experience in diagnostic assay development, next generation sequencing and bioinformatics pipeline development. A DVM or PhD is required for this position. This position will require attention to detail