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waves to unlock imaging capabilities at both surface and sub-surface levels. Recently, computationally intensive THz imaging methods have emerged, leveraging single-pixel detection and spatially
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waves to unlock imaging capabilities at both surface and sub-surface levels. Recently, computationally intensive THz imaging methods have emerged, leveraging single-pixel detection and spatially
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areas might include, but are not limited to, spatio-temporal models, time-series analysis, anomaly and change-point detection, Bayesian hierarchical models, statistical machine learning, optimal
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methods for Bayesian inference, inverse problems, uncertainty quantification, and scientific machine learning, with applications in environmental, scientific, and industrial imaging. The role/Te mahi
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detection o Bayesian methods and uncertainty quantification o Graph Neural Networks (GNNs) o Spatiotemporal data engineering o Digital twins and simulation o Demonstrated Applied AI for Healthcare and
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and written communication skills in English Background in biomarker analysis and/or compound specific isotope analysis and/or archaeometric dating techniques and Bayesian statistics. Personal
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, including explainability, auditability, and privacy Edge AI and model optimisation Physics-informed neural networks (PINNs) and surrogate modelling Time-series modelling and anomaly detection
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University of Singapore (NUS) is recruiting a post-doctorate Research Fellow. The successful candidate will work on developing Bayesian networks for predicting the joint probabilities of extreme events
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invites applications for a Senior Research Fellow to help lead a vibrant, internationally connected research programme spanning Bayesian infectious disease modelling, AI-driven epidemic forecasting, genomic
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specifically, your main tasks will include: Support our ongoing research on mechanistic modelling and Bayesian parameter inference from in vitro neural models. Combine theory, simulation, and data-driven