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Improving global climate models with quantum machine learning

Starting date

1 Dezember 2022

Duration of contract

4 years

Remuneration

up to German TVöD 13

Type of employment

Full-time (part-time possible)

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, https://www.usmile-erc.eu/)“, 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.

Your mission:

In this position, you will first develop a concept to use existing hybrid quantum/conventional machine learning algorithms to improve parameterizations of the general atmospheric circulation model ICOsahedral Nonhydrostatic (ICON). As a next step, you will implement these algorithms on quantum computers or simulators to develop quantum machine learning based parameterizations. Then, you will couple the improved parameterizations with the ICON model and evaluate the model’s performance using the ESMValTool developed at the Institute of Atmospheric Physics.

Improving global climate models using quantum machine learning techniques

  • development of parameterizations for global climate models using hybrid machine learning methods on conventional and quantum computers
  • integration of the newly developed parameterizations into a global climate model for the development of ICON-QML
  • performing ICON-QML simulations
  • evaluation of the model with the ESMValTool
  • application to a use case
  • publication of the results
  • documentation of results and software

Developing a concept for hybrid conventional/quantum computing techniques to improve climate models

  • literature research and application to conceptualize how to improve climate models using existing hybrid computational methods (conventional/quantum computing)
  • documentation and publication of results

Your qualifications:

  • Master/diploma in e.g., physics, mathematics, meteorology, data science, computer science or a comparable field of study
  • very good PhD in physics, meteorology, mathematics, data science, computer science or a comparable field of study
  • experience in working independently on complex research tasks
  • scientific excellence demonstrated by publication record or similar
  • experience working with very large data sets
  • advanced knowledge of machine learning techniques and data analysis
  • experience in or interest in quantum computing
  • very good programming skills, preferably Python
  • very good English language skills
  • willingness to travel
  • very good communication and teamwork skills
  • excellent analytical skills
  • experience in Earth system modeling, parameterization, and climate science is an advantage

Your benefits:

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.

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Technical contact

Mrs. Dr. Mierk Schwabe
Institute of Atmospheric Physics

Phone: +49 8153 28-4239

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Vacancy 69964

HR department Oberpfaffenhofen

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DLR site Oberpfaffenhofen

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