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Environmental Engineering (CEE). For more information, please visit: https://earthobservatory.sg , https://ntu.edu.sg/ase , and https://ntu.edu.sg/cee . We are looking to fill a total of 5 Research Fellow
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 6 days ago
, but are not limited to, robust and stochastic optimization, generative and foundation models, reliable and robust machine learning, uncertainty quantification, sequential decision-making, and AI systems
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uncertainty be modelled and propagated to support large-scale change analysis? Approach: You will combine advanced robotic localisation and high-resolution mapping methods with advanced machine learning feature
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. Jochen Peter ([email protected] ) or dr. Toni van der Meer ([email protected] ) Where to apply Website https://www.academictransfer.com/en/jobs/364079/phd-candidate-agents-of-doubt-a… Requirements
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uncertainty be modelled and propagated to support large-scale change analysis? Approach: You will combine advanced robotic localisation and high-resolution mapping methods with advanced machine learning feature
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sustainability. For more details, please view https://www.ntu.edu.sg/ase . The Wetland Carbon Lab at NTU’s Asian School of the Environment (ASE) focuses on understanding the role of wetlands and aquatic
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of statistical and probabilistic methods for the analysis of complex data and systems. Research topics include distributional modelling, stochastic dependence, extreme-value phenomena, uncertainty quantification
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structure and fisheries data. Understanding how these assumptions affect model performance and management advice is critical for identifying key sources of uncertainty and determining where methodological
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George Washington University | Washington, District of Columbia | United States | about 21 hours ago
, optimization, and decision-making under uncertainty. Specific Duties and Responsibilities: The successful candidate will conduct research for publication in leading journals and conferences, teach undergraduate
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their accuracy and performance. To develop inference methods that utilise these models and their gradients, to characterise biases and uncertainties, and to study scaling, the marginalisation of nuisance