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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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techniques from statistical physics, Bayesian inference, and complex systems theory to address challenges posed by noisy and incomplete data. Depending on the results obtained in the first year, the post can
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more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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of identifying excellent researchers and accelerating them in using AI to advance and disrupt Science or Engineering. Here ‘AI’ is interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics
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processes, Bayesian inference, signal models, sampling theory, sensing techniques, optimisation theory and algorithms, multi-modal data processing, high-performance computing, mathematical image analysis
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interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics; ‘Science’ covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine
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and statistics, with expertise spanning time series analysis, Bayesian inference, financial econometrics, and data analytics. As home to one of the strongest forecasting research groups worldwide, we
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emerging areas, and currently covers the following topics: Signal and image processing theory Statistical signal processing, non-stationary processes, Bayesian inference, signal models, sampling theory
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or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source