Research

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Climate Informatics is a challenging and promising research field where a concentrated effort will have a high impact both to advance science and to address climate change topics of critical importance for the society. The group combines innovative data science methods from different fields (graphical models, causal inference, nonlinear dynamics, deep learning) and closely works with experts in the climate sciences.

Contribute your own ideas to this exciting field and join for a PhD or Postdoc!

Current third-party projects

CausalEarth

ERC Starting Grant 2021 — 2025

climate_informatics_logo_big.svgCausalEarth is an interdisciplinary project, aiming to improve our understanding of the causal interdependencies between major drivers (modes) of climate variability by developing novel machine learning-based causal inference methods for both observations and model data. The modes’ interdependencies are characterized by common drivers, indirect effects, nonlinearities, nonstationarity, and all these between highly complex spatio-temporal phenomena. CausalEarth will develop causal inference methods that account for such complex characteristics and apply them to observational and climate model data to improve our understanding of the climate system and climate change.

IMIRACLI

H2020 PhD training network 2020-2023

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IMIRACLI is a joint European PhD training network comprising a dozen institutions and industrial partners. The overall goal is to merge AI, machine learning and climate science to deliver a breakthrough in our understanding of the impact of aerosol-cloud interactions on climate. In the Climate Informatics Group the focus is on method development to address the problem of learning cause and effect relationships from observational data. These methods help to deepen our understanding of complex dynamical processes in the climate system such as the interactions of aerosols and clouds. There are two PhD projects involved, one on causal inference in the presence of multiple time scales and one on causal inference in the presence of latent variables.