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-doctoral Associate to work on a fascinating project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a
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. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement learning, and Data-driven modeling and control. The Post-Doctoral associates will be based at NYU Abu Dhabi and
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, Reinforcement learning, and Data-driven modeling and control. The Post-Doctoral associates will be based at NYU Abu Dhabi and will directly report to Prof. K.J.Kyriakopoulos, in close collaboration with other
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience. The ideal candidate is self-motivated and can work
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SolidWorks experience considered an advantage. Publish research findings in high-quality peer-reviewed journals. Work collaboratively with faculty, postdoctoral researchers, students, and other members of a
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collaborations. Applicants must hold a Ph.D. in science, engineering, or a closely related field awarded before the start date, and is within the first five years post-receiving a PhD. Competitive candidates will
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, and the capacity to work effectively in a collaborative academic research environment. Experience mentoring undergraduate or graduate students, assisting with research proposals, and supporting
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well as scientific creativity are essential. Applicants with strong mathematics training are particularly encouraged to apply. The position will also require the ability to collaborate with other researchers on other
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for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized
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of academic success for a Post-Doctoral Associate position. The research will focus on the development and use of machine learning and advanced molecular dynamics techniques for molecular simulations and to