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to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence approaches. Formal models are directly applied in real experimental facilities. We are seeking a Post
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the area of computational heat transfer and machine learning for radiative transfer in scattering media. The successful applicant will use machine learning for solar photovoltaic (PV) and concentrated solar
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the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital twin
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, statistical signal processing, optimization theory, machine learning and artificial intelligence. The candidate is expected to actively participate in experimental work focused on building datasets of channel
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construction monitoring and decision-making. The role involves leveraging machine learning and data visualization techniques to analyze and track construction progress using diverse datasets, such as images
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Experience in machine-learning modeling for solid mechanics applications Experience in the development and coupling of numerical methods for solid mechanics modeling Post-Doctoral Associate Employment at NYUAD
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-assisted MIMO communication systems using tools from information theory, statistical signal processing, optimization theory, machine learning and artificial intelligence. The candidate is expected
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: Developing and implementing digital twin platforms and graphical user interfaces (GUIs) to support construction monitoring and decision-making. The role involves leveraging machine learning and data
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at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control, Reinforcement learning, and Data-driven modeling and control. The Post
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discrete choice models, machine learning techniques, big data, and optimization. For consideration, applicants need to submit a cover letter with reference to the position(s) they are applying