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at NTU are looking for a Research Fellow (RF) to carry out in research in probabilistic machine learning, causal discovery and GenAI, by exploring cutting-edge approaches such as causal representation
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methods for reasoning over multi-agent interactions. This Research Assistant position will contribute to research exploring how temporal knowledge representation, abductive and probabilistic reasoning and
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monitoring, earthquake physics, ground-motion modelling, site response, exposure and vulnerability, and probabilistic seismic hazard and risk assessment, working to strengthen Singapore’s national capabilities
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will focus on efficient probabilistic analysis of high-dimensional and dynamic systems, including advanced sampling, surrogate modelling, and AI or machine-learning methods where appropriate. Key
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: The fellowship aims to develop and validate a probabilistic methodology for hosting-capacity assessment in distribution grids, taking the RECEP approach as a baseline and extending it with Monte Carlo scenario
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processes under different biological conditions. Apply statistical learning, deep learning and probabilistic modelling approaches to large-scale cancer datasets. Evaluate and benchmark computational methods
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on a project in the area of power system risk modeling and assessment. Key Responsibilities: Develop a probabilistic security assessment framework for heterogeneous operational uncertainties and
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-scale inverse problems by combining interpretable high-level probabilistic models, multi-physics data integration, and modern machine learning. The resulting methods will be validated on groundwater
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studies, systematic reviews, and observational data into model parameters; assisting with model programming, calibration, validation, and documentation; conducting deterministic and probabilistic
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skills. Experience in probabilistic analysis and uncertainty quantification within an engineering context, a track record of high-quality peer-reviewed research, and the ability to work independently and