419 machine-learning-"https:"-"https:"-"https:" positions at Nanyang Technological University
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, such as scientific computing and scientific machine learning, and promote interdisciplinary collaboration among the disciplines of mathematics, computer science, and engineering. Candidates with strong
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related field. Strong background in statistical modelling, high-dimensional data analysis, and machine learning. Proficiency in R; Python or related computational experience is advantageous. Good analytical
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research in formal verification, machine learning and artificial intelligence system assurance. The successful candidate will develop new techniques and tools for analysing, verifying and improving
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equivalent. More than 2 papers published at top AI/Machine learning conferences Experience of deep learning and machine learning Good communication and coordination skills Positive working attitude and a good
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. More than 2 papers published at top AI/Machine learning conferences Experience of deep learning and machine learning Good communication and coordination skills Positive working attitude and a good team player
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of machine learning/deep learning for signal processing and receiver design is desirable. Experience with SDR platforms, particularly Ettus USRP devices, and GNU Radio/UHD development is an advantage. Strong
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processing, estimation theory, detection and classification, waveform-based geolocation, and machine learning/deep learning, with demonstrated research experience in passive localization and tracking using
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An Institute of Distinction: Leading the Future of Education, and our mission to Inspire Learning, Transform Teaching and Advance Research. Read more about NIE here . The Academic Computing and Information
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intelligence, machine learning, deep learning, environmental modelling, climate-health research, early warning systems, or related fields. Have strong programming and computational skills in Python, R, MATLAB
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computer systems is cognitivist in nature. The advancements in LLMs appear promising in bridging this dialogic gap in feedback and learning via computer systems. This study aims to test the efficacy of LLMs