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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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, 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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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
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of substitution models using large dataset, successful applicants must then have a PhD and demonstrated experience in discrete choice models, machine learning techniques, big data, and optimization
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MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless communications Hardware-constrained signal processing