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Climate Modelling

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Climate Modelling

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Research Frontiers in Machine Learning Climate Pattern Recognition

Development of deep neural networks and supervised learning algorithms to identify complex climate patterns and teleconnections from high-dimensional observational and model data.

Neural Operators for Multiscale Atmospheric Dynamics

Learned Climate Surrogates Beyond Equilibrium States

Graph Neural Networks in Global Circulation Modeling

Subgrid Physics Inference Through Deep Learning

Attention Mechanisms for Spatiotemporal Climate Teleconnections

Physics-Informed Neural Networks in Paleoclimate Reconstruction

Causal Discovery in High-Dimensional Climate Systems

Generative Models for Extreme Weather Pattern Synthesis

Transfer Learning Across Climate Models and Resolutions

Uncertainty Quantification in Neural Climate Emulators

All Climate Modelling PhD categories