Job description

The Department of Climate Modelling at the University of Bremen invites applications for a Post-Doctoral position in the field of Machine learning (ML) - based turbulence parameterizations for climate models under the supervision of Prof. Veronika Eyring. This position is announced as part of a broader call of positions on ML-based climate science funded by the Gottfried Wilhelm Leibniz Prize 2021 (https://www.dfg.de/en/funded_projects/prizewinners/leibniz_prize/2021).

Despite significant progress in climate modelling over the last few decades, systematic biases and substantial uncertainty in the model responses remain. For example, the range of simulated effective climate sensitivity - the change in global mean surface temperature for a doubling of atmospheric CO2 - has not decreased since the 1970s. A major cause of this are differences in the representation of clouds and other processes occurring at spatial scales smaller than the model grid resolution. These need to be approximated through parameterizations that represent the statistical effect of a process at the grid scale of the model. This impacts models’ ability to accurately project global and regional climate change, climate variability, extremes and impacts on ecosystems and biogeochemical cycles. While efforts are underway to develop convection resolving high resolution global climate models where some of the physical processes can be explicitly modelled, for model experiments that need to cover long time periods or for those that require additional complexity beyond the traditional physical model setup, coarser Earth system model simulations will continue to be required. A promising way forward that is addressed in this position is to develop machine learning (ML) and especially deep learning-based parameterizations for the ICOsahedral Nonhydrostatic (ICON) atmospheric general circulation model. In this position the focus will be on the development of an ML-based turbulence parametrization for ICON.

Field of activity:

The candidate will perform and use high-resolution simulations and observations to develop a machine learning based parameterization. The ML-parameterization will be implemented into the ICON atmospheric general circulation model.

• High-resolution simulations with ICON to serve as training data for the development of the ML-based parameterizations
• Development of ML and especially deep learning-based turbulence parameterizations for ICON
• Implementation into ICON
• Performing corresponding climate simulations with ICON-ML
• Evaluation of the resulting ICON-ML with the Earth System Model Evaluation Tool (ESMValTool)
• Documentation and software as open source

Requirements

• Master/diploma or equivalent degree in physics, data science, computer science or similar field
• PhD in physics, data science, computer science or similar field
• Excellent programming skills (preferably python), including object oriented and parallel programming
• Experience in machine learning, data analysis, and large data sets
• Interest in climate research
• Excellent analytical skills, and the ability to work both, independently and as part of a team
• Enthusiasm, motivation, and creativity
• Fluency in English (written and spoken) (at least C1 – CEF)
• Readiness to travel

Additional (desired) skills:

• Experience in Earth system modelling, parameterization and climate science is an advantage

General hints

Your Benefits

At the “Climate Modelling” department we provide excellent facilities with opportunities to work with world-renowned experts in the field of Earth system modelling and machine learning. The department develops innovative methods, including machine learning techniques to improve Earth system models and their evaluation with observations with the aim to better understand and project climate change. The Postdoc can benefit from a dynamic group and close collaboration with the „Earth System Model Evaluation and Analysis” department of the DLR Institute of Atmospheric Physics as well as from interactions with the 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/). The department is strongly linked to international research activities within the World Climate Research Programme (WCRP), with substantial contributions particularly to the Coupled Model Intercomparison Project (CMIP), and contributes regularly to international climate of the Intergovernmental Panel on Climate Change (IPCC).

General Information:

As the University of Bremen intends to increase the proportion of female employees in science, women are particularly encouraged to apply. Applicants with a migratory background are highly welcome. Disabled candidates will receive preferred consideration over mainly equally qualified contenders.

Contact

Questions concerning scientific issues:
Prof. Dr. Veronika Eyring, veronika.eyringprotect me ?!uni-bremenprotect me ?!.de  

Please send your application (cover letter, cv, a minimum of two reference letters, and copy of your degree certificates) until April ^7th, 2023 by indicating the job id A253/22 to:

University of Bremen / FB1
Team assistant of Prof. Eyring
Mrs S. Smit
Universitätsallee GW1, Room A1185
28359 Bremen
Germany

or by e-mail: smitprotect me ?!iup.physik.uni-bremenprotect me ?!.de, phone: +49 421 218 62118

Paper-based applications are only required as a copy (no folders); they will be destroyed after the closure of the application procedure.