Physicist, Meteorologist, Mathematician, or similar (f/m/x)

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PhD Position

Enter the fascinating world of the German Aerospace Center (Deutsches Zentrum für Luft- und Raumfahrt; DLR) and help shape the future through research and innovation! We offer an exciting and inspiring working environment driven by the expertise and curiosity of our 11,000 employees from 100 nations and our unique infrastructure. Together, we develop sustainable technologies and thus contribute to finding solutions to global challenges. Would you like to join us in addressing this major future challenge? Then this is your place!

For our Institute of Atmospheric Physics in Oberpfaffenhofen near Munich we are looking for:

Physicist, Meteorologist, Mathematician or similar (f/m/x)

Developing machine learning-based shallow convection parameterizations for the ICON climate model

What to expect:

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 a PhD Position in the field of machine learning-based parameterizations. The candidate will be based at DLR-IPA in Oberpfaffenhofen, and supervised by Prof. Veronika Eyring, head of the department and Professor of Climate Modelling at the University of Bremen. Close collaborations also exist with the Technical University of Munich (TUM). The position is to be filled as soon as possible, for a duration of 3 years.

Shallow cumuli are convective clouds that generally do not precipitate substantially and whose tops rarely exceed 3 km in altitude. While these clouds can be frequently observed at midlatitudes during warm summer days, they are found on a daily basis over the tropical oceans where they play a central role in the water cycle and energy balance. Despite their importance, Earth system models (ESMs) still have difficulties representing their impact on climate, in particular in a warming atmosphere. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report in 2023 indeed assessed that climate feedbacks involving low-level tropical clouds remain very uncertain. These uncertainties are to a large extent related to the fact that ESMs generally operate at resolutions too coarse to resolve shallow cumuli: grid spacings smaller than 500 m are required to explicitly represent shallow clouds whereas climate projections are generally performed at resolutions on the order of 100 km. As a consequence, the influence of shallow cumuli on the resolved flow must be approximated using mathematical models known as parameterisations. Since available computational resources do not permit climate simulations at resolutions fine enough to explicitly represent these clouds, improving our understanding of tropical shallow cloud-climate feedbacks thus necessitates the development of enhanced parameterisations capable of modeling their impact with high accuracy. In an attempt to overcome the limitations of traditional parameterisations, this project aims at designing novel data driven, Machine Learning (ML) based models to parameterise the effects of shallow clouds in ESMs.

The candidate will be part of an international team of the European Research Council (ERC) Synergy Grant on "Understanding and Modelling the Earth System with Machine Learning (USMILE, https://www.usmile-erc.eu/)". During your PhD you will utilize high-resolution ICON simulations (large eddy simulations - LES) to train ML algorithms to represent the effect of shallow convection on the simulated climate in a coarser version of the ICON-ML Model that is developed by the group.

The successful candidate will be expected to:

• Perform and analyse very high-resolution numerical simulations in the trade wind region using ICON-LES (Large-Eddy Simulation)
• Develop ML algorithms to parameterize the effects of shallow clouds simulated with ICON-LES
• Implement it into the general circulation model ICON-ML and execute climate simulations
• Evaluate the ICON-ML results with the Earth System Model Evaluation Tool (ESMValTool)
• Document the work (publications) and provide software as open source

At the DLR Institute of Atmospheric Physics we provide excellent facilities with opportunities to work with world-renowned experts in the field of Earth system modelling and observations. You will be part of the Earth System Model Evaluation and Analysis Department which develops and applies innovative methods, including ML techniques, for the analysis of Earth system models in comparison to observations. 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. The department is strongly linked to international research activities within the World Climate Research Programme (WCRP), with substantial contributions to CMIP. We are striving to increase the proportion of female employees and therefore particularly welcome applications from women.

Please submit your application including a letter of motivation explaining your interest in this position, curriculum vitae, publication list, documentation of academic degrees and certificates, and two letters of reference.

What we expect from you:

• Master's degree or equivalent in physics, mathematics, meteorology or similar field with adequate educational background for a PhD thesis in physics
• Very good programming skills (preferably Python) and experience in data analysis
• Interest in climate research and Earth system modelling
• Enthusiasm, motivation and creativity
• Fluency in English (written and spoken)

• Experience in machine learning and climate modelling is an advantage

What we offer:

DLR stands for diversity, appreciation and equality for all people. We promote independent work and the individual development of our employees both personally and professionally. To this end, we offer numerous training and development opportunities. Equal opportunities are of particular importance to us, which is why we want to increase the proportion of women in science and management in particular. Applicants with severe disabilities will be given preference if they are qualified.

Further information:

Starting date: as soon as possible
Duration of contract: 3 years
Type of employment: Teilzeit

Remuneration: Up to 75 % of the German TVöD 13

Vacancy-ID: 98767

Contact:

Dr. Julien Savre-Piou
Institute of Atmospheric Physics
Email: julien.savre-piou@dlr.de