48467: Research Software Engineer or similar - Development of the ESMValTool

German Aerospace Center (DLR)

Oberpfaffenhofen, Germany

Work group:

Institute of Atmospheric Physics

Area of research:

Scientific / postdoctoral posts

Job description:

The Institute of Atmospheric Physics develops innovative methods for the evaluation and analysis of Earth system models in comparison to observations with the aim of better understand and project the Earth system.

The evaluation and ensemble analysis of Earth system models is crucial for model improvements and a prerequisite for reliable climate projections of the 21st century to be used as guidelines for climate policy.

Together with many international partners, the institute is leading the development of an open-source software tool to take the evaluation of complex Earth System Models with observations to the next level (ESMValTool). The goal of this internationally well-recognized effort is to improve comprehensive and routine evaluation of Earth System models participating in the Coupled Model Intercomparison Project (CMIP). Together with partners from Jena, Valencia and New York, the institute has recently been awarded a renowned and prestigious European Research Council (ERC) Synergy Grant for the project „Understanding and Modelling the Earth System with Machine Learning" (USMILE). Within this project it is planned to close gaps in the understanding of small scale physical and biological processes such as clouds, stomata and microbes within the climate system. The aim of USMILE is to better understand and model the changes and feedbacks of these processes and their impacts on the Earth’s ecosystems with machine learning.

Within the framework of the research environment outlined above, the position involves the following tasks:

Technical development of the ESMValTool with focus on software optimization extension and optimization of the ESMValTool technical infrastructure porting the ESMValTool to systems connected to the Earth System Grid Federation (ESGF) support of the ESMValTool development cycles, software engineering and error analysis implementation and performance of regular tests on different computing platforms development of efficient analysis methods for large data volumes (petabyte scale) Development of machine learning techniques in the ESMValTool framework technical development of innovative big data analysis methods technical support of department scientists for implementation and development of machine learning techniques on different high performance computing platforms

Please apply via recruiter’s website.

Quote Reference: 48467