Research Software Engineer (f/m/x)

The DLR Institute of Atmospheric Physics studies the physics and chemistry of the global atmosphere from the ground to the upper edge of the middle atmosphere at an altitude of about 120 km. As an institute of the German Aerospace Center, the institute addresses questions regarding the atmosphere relevant to DLR in the HGF programs of Aeronautics, Space, Transport and Energy. To this end, the institute covers the entire spectrum of methods ranging from sensor development, observations on different spatial scales (local to global), analysis, theory to numerical modeling including future projections.
The Earth System Model Evaluation and Analysis Department develops innovative methods to improve Earth system models and their evaluation with observations with the aim to better understand and project climate change. The evaluation and ensemble analysis of Earth system models is essential to continuously improve the models and a prerequisite for reliable climate projections of the 21st century, which are used in climate policy guidelines. In order to improve the routine and comprehensive evaluation of climate models, the department is leading the development of the Earth System Model Evaluation Tool (ESMValTool). A major focus of the department is the development and application of machine learning (ML) methods to improve the understanding and modeling of the Earth system.

Quantum computers are one of the ground-breaking novel technologies of the 21st century. They will make it possible to perform calculations and simulations that conventional computers would need a prohibitive amount of time for. With the Quantum Computing Initiative, DLR has begun a concerted effort to build quantum computers for Europe and unlock their potential for pioneering applications.

Building on work that is done in the European Research Council (ERC) Synergy Grant on „Understanding and Modelling the Earth System with Machine Learning (USMILE,“, a new project on “Improving Climate Models with Quantum Machine Learning for Robust Technology Assessments and Mitigation (KLIM-QML)” within the Earth System Model Evaluation and Analysis Department will develop a prototype for a quantum machine learning (QML) based climate model where physical sub-grid scale parametrizations are replaced with QML approaches. The unique opportunities that quantum computing offers are explored to help make climate models and their development process better and faster. This work will be done in close collaboration with industry partners. 

In this position, you will further develop hybrid conventional/quantum algorithms for learning parameterizations for a global climate model are technically and support the KLIM-QML team in the application and implementation of these algorithms into the ICON climate model. In addition, you will run simulations with the newly developed ICON-QML and evaluate them in comparison with observations using the ESMValTool developed at the institute.

  • Technical development of hybrid algorithms on conventional and quantum computers for learning parameterizations for a global climate model
    • Development and adaptation of quantum machine learning algorithms for learning parameterizations for global climate models on hybrid quantum/conventional computers
    • Technical support in coupling the developed QML parameterizations into the ICON climate model in order to develop ICON-QML
    • Implementation of ICON and ICON-QML simulations
    • Technical support of the department members in the implementation and development of machine learning methods on various high performance and quantum computers
    • Publication of results in journals on machine learning and quantum computing
    • Documentation of software and results
  • Technical support for the development and application of the ESMValTool
    • Technical support in developing diagnostics for the evaluation of climate models with the ESMValTool
    • Technical support in the evaluation of ICON and ICON-QML simulations with the ESMValTool
    • Documentation of the results
  • degree (Bachelor or Master) in computer science, scientific computing, data science or comparable field 
  • good knowledge of machine learning
  • very good knowledge of programming with Python
  • knowledge of software engineering incl. tools
  • interest in quantum computing, ideally previous knowledge of it
  • knowledge of Unix / Linux
  • very good written and spoken English
  • willingness to travel
  • experience in processing large amounts of data is a plus
  • experience in the implementation of large software development projects is desired

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x).Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

DLR - Helmholtz / Deutsches Zentrum für Luft- und Raumfahrt

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Location: Germany, Oberpfaffenhofen
Categories: Computer Sciences, Data Science, Machine Learning, Programming, Quantum Computing, Scientific Computing, Software Engineering,


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