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networks. You should have experience of building machine learning models for environmental applications. A high level of data science and computational expertise is essential, as is experience with Bayesian
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The role The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model
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climate data, simple physics-based models, and AI to deliver more accurate projections of how our climate will warm and recover in a net-zero future. As part of this project, you will contribute to develop
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project developing Bayesian causal inference methods for mediation analysis using Electronic Health Records (EHR) data. The Research Fellow will design and implement Bayesian methods and software