Physicist, Mathematician, Computer Scientist or similar (f / m / x) - Forschung, Ingenieur

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For our Institute of Atmospheric Physics in Oberpfaffenhofen we are looking for Physicist, Mathematician, Computer Scientist or similar (f / m / x), Improving climate models and analysis with machine learning and spaceborne Earth observations

Das erwartet Sie :

The Department Earth System Model Evaluation and Analysis of the Institute of Atmospheric Physics at the German Aerospace Center (DLR-IPA) in collaboration with the Climate Modelling Department of the Institute of Environmental Physics (IUP) at the University of Bremen invites applications for 8-10 PhD positions in the field of improving climate models and analysis with machine learning and spaceborne Earth observations. The PhD candidates will be supervised by Prof. Veronika Eyring (https : / / www.pa.op.dlr.de / ~ / VeronikaEyring / ), head of the department and Professor of Climate Modelling at the University of Bremen. All PhD candidates will also be co-supervised by a leading expert in this field from the ERC Synergy Grant “Understanding and Modelling the Earth System with Machine Learning (USMILE, https : / / www.usmile-erc.eu / (https : / / www.usmile-erc.eu / ))” or from another collaborating institution. An extended research stay at the co-supervisor’s institution is envisaged. Based on the candidate’s experience and interest, PhD positions in the following broad areas of research are available :

• developing and enhancing ML-based parametrizations for climate models to reduce systematic errors and to enhance projection capabilities with innovative ML methods (e.g., physical constraints, eXplainable Artificial Intelligence, uncertainty quantification, causal deep learning)
• developing data-driven equation discovery methods to learn interpretable and physically consistent equations from high-fidelity datasets to enhance understanding and representation of subgrid-scale processes (e.g., clouds, convection) in climate models
• developing and benchmarking foundation models for selected climate modeling tasks
• developing ML-techniques for improved understanding and detection of extreme events
• developing innovative methods, including ML, to enhance the evaluation and analysis of climate models in comparison to observations using the Earth System Model Evaluation Tool (ESMValTool, https : / / esmvaltool.org / ).

At the DLR Institute of Atmospheric Physics and the University Bremen we provide excellent facilities with opportunities to work with world-renowned experts in the field of Earth system modelling, Earth observations, and machine learning. The department develops an ML-enhanced version of the Icosahedral nonhydrostatic (ICON) model alongside an evaluation system (ESMValTool) that supports the comprehensive evaluation of Earth system models in comparison to observations and to other models participating in the Coupled Model Intercomparison Project (CMIP). The ultimate goal is to improve climate models and projections with machine learning and spaceborne Earth observations for actionable climate science and technology assessments in aeronautics, space, transport, and energy research. For further reference of our work, please see the Veronika’s publications at https : / / scholar.google.at / citations?user=Y3i87foAAAAJ&hl=de (https : / / scholar.google.at / citations?user=Y3i87foAAAAJ&hl=de) and our Github repository at https : / / github.com / EyringMLClimateGroup / . (https : / / github.com / EyringMLClimateGroup / )

Please submit your application including a letter of motivation explaining your research interest for the selected topic, curriculum vitae, publication list if available, documentation of academic degrees and certificates, and two letters of reference. We are striving to increase the proportion of female employees and therefore particularly welcome applications from women.

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Unser Angebot :

Das DLR steht für Vielfalt, Wertschätzung und Gleichstellung aller Menschen. Wir fördern eigenverantwortliches Arbeiten und die individuelle Weiterentwicklung unserer Mitarbeitenden im persönlichen und beruflichen Umfeld. Dafür stehen Ihnen unsere zahlreichen Fort- und Weiterbildungsmöglichkeiten zur Verfügung. Chancengerechtigkeit ist uns ein besonderes Anliegen, wir möchten daher insbesondere den Anteil von Frauen in der Wissenschaft und Führung erhöhen. Bewerbungen schwerbehinderter Menschen bevorzugen wir bei fachlicher Eignung.

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