Prof. Veronika Eyring

• Affiliation: DLR, University of Bremen
• Head of the Earth System Model Evaluation and Analysis Department at DLR, Institute of Atmospheric Physics and Professor of Climate Modelling at the University of Bremen – WP1-4
• USMILE Corresponding PI
• Personal webpage

Gunnar Behrens

• Affiliation: DLR & CU
• PhD Researcher – WP2
• Machine learning based parametrizations of sub-grid-scale processes in climate models to enhance understanding on convection and its predictability.

Dr. Pauline Bonnet

• Affiliation: DLR
• Postdoctoral Researcher – WP2
• Machine learning based automated parameter tuning of global climate models with satellite data
• Personal webpage

Max Adriaan Bouman

• Affiliation: DLR
• PhD Researcher – WP4
• Machine learning based automated parameter tuning of global climate models focusing on land atmosphere interactions.

Kevin Debeire

• Affiliation: DLR (Oberpfaffenhofen & Jena)
• PhD Researcher – WP3
• Causal discovery methods for climate model evaluation and for constraining uncertainty in climate projection.

Dr. Evgenia Galytska*

• Affiliation: University of Bremen, DLR
• Research Scientist – WP3
• Understanding Arctic-midlatitude teleconnections in CMIP6 models using causal networks.

Arthur Grundner

• Affiliation: DLR & CU
• PhD Researcher – WP2
• Improvement of cloud parametrizations in a climate model by utilizing high-resolution data for the training of machine learning parametrizations.

Katharina Hafner*

• Affiliation: University of Bremen, DLR
• PhD Researcher – WP2
• ML-based radiation parametrizations for ICON

Elias Haslauer

• Affiliation: DLR
• PhD Researcher – WP2
• ML-based parameterizations of gravity waves in the atmosphere.

Helge Heuer

• Affiliation: DLR, University of Bremen
• PhD Researcher – WP2
• ML-based cloud and convection parametrizations for ICON.

Dr. Fernando Iglesias-Suarez

• Affiliation: DLR
• Postdoctoral Researcher – WP2
• Developing and applying machine learning techniques to advance climate models and analysis, understanding links and interactions with other parts of the Earth system to address the scientific challenges of global change.
• Personal webpage

Arndt Kaps

• Affiliation: DLR
• PhD Researcher – WP1
• Machine learning based approaches to evaluate clouds in climate models to leverage the information available from satellites and bridge the gap to model data.

Soufiane Karmouche*

• Affiliation: University of Bremen, DLR
• PhD Researcher – WP3
• Investigating teleconnections between major modes of climate variability with causal discovery.

Birgit Kühbacher

• Affiliation: DLR
• PhD Researcher – WP2
• Causal ML-methods for parametrization in climate models.

Aytaç Paçal

• Affiliation: DLR
• PhD Researcher – WP3
• Detecting and understanding extreme weather events using machine learning.

Lukas Ruhe*

• Affiliation: University of Bremen, DLR
• PhD Researcher – WP3
• Machine learning based analysis and detection of droughts in climate projections.

Dr. Manuel Schlund

• Affiliation: DLR
• Postdoctoral Researcher – WP4
• Constraining uncertainties in climate model projections with machine learning methods.

Dr. Mierk Schwabe

• Affiliation: DLR
• Research Scientist – WP2
• Parametrizations of atmospheric gravity waves in climate models based on machine learning.

Dr. Katja Weigel*

• Affiliation: University of Bremen, DLR
• Research Scientist – WP3
• Analysis and detection of extreme events with the focus on droughts in climate model and reanalysis data using classical indices as well as testing new tool like the Maximal Divergent Interval (MDI) algorithm.