Machine Learning & Data Science

Machine Learning for Earth and Climate Sciences

Model and understand the Earth system with Machine Learning and Process Understanding more

ELLIS - the European Laboratory for Learning and Intelligent Systems - is a pan-European AI network of excellence which focuses on fundamental science, technical innovation and societal impact. Founded in 2018, ELLIS builds upon machine learning as the driver for modern AI and aims to secure Europe’s sovereignty in this competitive field by creating a multi-centric AI research laboratory. ELLIS wants to ensure that the highest level of AI research is performed in the open societies of Europe and follows a three-pillar strategy to achieve that. more

The awarded 2019 European Research Council (ERC) Synergy Grant USMILE aims to advance understanding and modeling of the Earth system with machine learning (ML), one of the important approaches of artificial intelligence (AI). The prestigious award will support the team’s groundbreaking work in rethinking the development and evaluation of Earth system models, which are the basis for understanding and projecting climate change. more

Information from diverse and heterogeneous data streams must be interpreted jointly to understand complex extreme event impacts such as e.g the Russian Heatwave 2010. more

...more about casual discovery in the complex Earth system

Runge, J. et al. (2019). Inferring causation from time series in Earth system sciences. doi:10.1038/s41467-019-10105-3 more

Explore and develop new deep learning approaches to address relevant Earth system dynamics related to localized impacts of extreme events through AI-based understanding, quantification and prediction more

eXtreme events : Artificial Intelligence for Detection and Attribution to better assess and predict the influence of climate change on extreme weather using novel artificial intelligence methods more

...more about ML challenges for Earth System Sciences

Reichstein, M. et al. (2019). Deep learning and process understanding for data-driven Earth system science. doi:10.1038/s41586-019-0912-1 more