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Field
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addresses the need for data-driven and hybrid modeling approaches that combine physics-based knowledge with artificial intelligence (AI) algorithms for accurate, interpretable, and robust health state
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for measurement challenges (e.g., for small-sample calibration or for accelerated algorithms), (b) identifying and investigating aberrant response behavior (such as rapid guessing, cheating, or careless responding
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operationally safe position in real-time. This research focuses on real-time multi-objective optimization of wells, that may be achieved with a mixture of algorithmic and machine-learning approaches. Updating
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. Characterise and optimise the algorithm for signal data processing for real-time cell analysis and sorting Develop a user-interface platform for end-user testing of clinical samples. Support lab procurement and
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industry and international partners. For more details, please view https://www.ntu.edu.sg/atmri . We are looking for a Research Fellow to conduct research on air traffic management (ATM) algorithms and data
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radiation therapy. The primary aim of this research is to develop real-time target tracking and/or dynamic imaging algorithms for implementation within radiotherapy and medical imaging. Within our research
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systems • Translate predictive control models into deployable algorithms for live building operation • Lead and support the full-scale deployment and testing of control strategies in an operational
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interpretable autonomous experimentation systems remains a major research challenge. The successful candidate will develop reinforcement-learning and decision-making algorithms for autonomous laboratory platforms
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validation of artificial intelligence models. Support in the acquisition and management of data collected by the group in a clinical or laboratory setting. Experimental evaluation of algorithms, development
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manage research datasets, including development of analytic workflows, REDCap data collection tools, algorithm development, and validation of NLP pipelines · lead the development of scholarly outputs