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existing models struggle to capture this complex, multiscale phenomenon efficiently. This project will develop a novel, physics-informed surrogate model using Bayesian machine learning to predict gas
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modelling, ecological forecasting, time-series analysis, Bayesian statistics, and statistical programming (primarily in R and Stan). You will gain experience working with large, long-term, multidimensional
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, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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or more of DFT, molecular dynamics, multiscale/materials modelling, or machine learning for the sciences including, Bayesian methods, uncertainty quantification, scientific AI workflows, automated discovery
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on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic data. The aim is to enable meaningful analyses, such as identifying disease
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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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for Bayesian/statistical modelling with uncertainty analysis or programming skills in Python, MATLAB or equivalent and a background in hydraulic, kinetic or systems models. Also highly desirable to have
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one or more of the following areas is meriting: Bayesian statistics, mathematical modelling, probabilistic machine learning, deep learning, large language models. Rules governing PhD students are set
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criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building