Physicist, Mathematician, Computer Scientist or similar (f/m/x)

The PhD candidate will be supervised by Prof. Veronika Eyring, a leading expert in improving climate models with machine learning and Distinguished Affiliated Professor at the Technical University of Munich (TUM), and co-supervised by Prof. Tobias Osborne from the University of Hannover, a leading expert in quantum information theory. The PhD is part of the Quantum Fellowship Program of the DLR Quantum Computing Initiative, which offers education in quantum computing and further topics as well as excellent networking opportunities.

Despite significant progress in climate and Earth system modelling over the last few decades, systematic biases and substantial uncertainty in the model responses remain. A major cause of this is 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 parametrisations that represent the statistical effect of that process at the grid scale of the model. New approaches are required that exploit opportunities from increasing computational power and new computational paradigms. The Quantum Computing for Earth System Modelling group within the department develops a prototype for a climate model based on quantum machine learning (QML), replacing subgrid-scale physical parameterisations with QML approaches. The unique opportunities offered by quantum computing are explored to improve and accelerate climate modelling and its development process.

The candidate will explore the potential of quantum computing to accelerate climate models, in particular the solving of selected partial differential equations (PDEs), as a collaborative project between DLR-IPA and the University of Hannover. The PhD thesis will particularly focus on identifying the greatest possible quantum speedup to a subroutine or subtask of the climate modelling computational pipeline and find ways to apply Noisy Intermediate-Scale Quantum or “NISQ-friendly” techniques to the equations arising in the field of climate modelling.

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.