Senior Research Fellowship in Machine Learning for Weather and Climate Science

Climate change is one of the key challenges of the 21st century, and climate action is one of the UN’s sustainable development goals. The objective of this post is to work with climate and environmental data and work with collaborators toward better statistical climate modeling and data-driven forecasting. This project lies at the intersection of climate/environmental science and statistical machine learning. Research could include building scalable probabilistic surrogate models, scalable probabilistic message passing algorithms, building efficient inference algorithms. Application areas could be statistical downscaling, data assimilation, modelling of weather or oceans, nuclear fusion.

Check out more details about the role and application.