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Research Assistant (PhD) (f/m/d)
Institute for Environmental Physics (IUP)

Faculty 1 Physics/Electrical Engineering



Classifications E 13 - part-time 66%
Reference number: A267/21
Closing date: 12/09/2021
Public vacancy

The University of Bremen, Faculty 1 (Physics/Electrical Engineering),Institute for Environmental Physics (IUP) - under the condition of job release – at the earliest possible date – offers the position of a

Research Assistant (PhD) (f/m/d)
German Pay Scale EG 13 TV-L
with 66 % of the full working time per week, limited for 3 years.

The time limitation is subject to the scientific qualification according to the Act of Academic Fixed-Term Contract, §2 (1) (WissZeitVG – Wissenschaftszeitvertragsgesetz). Therefore, candidates may only be considered if they dispose of the respective scope of qualification periods, according to §2 (1) WissZeitVG.

 


Job description

The Department of Climate Modelling at the University of Bremen invites applications for a PhD Position in the field of Machine learning (ML) - based parametrizations 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 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. This impacts models’ ability to accurately project global and regional climate change, climate variability, extremes and impacts on ecosystems and biogeochemical cycles. High-resolution, cloud resolving models alleviate many biases of coarse-resolution models for deep clouds and convection, wave propagation and precipitation, but they cannot be run at climate timescales for multiple decades or longer due to high computational costs. New approaches are required that exploit opportunities from increasing computational power and ML, utilizing high-resolution simulations and observational data.

In this thesis, the ICOsahedral Nonhydrostatic (ICON) atmospheric general circulation model is further developed to accelerate radiation and to improve radiation parametrizations. Radiation is a key factor in controlling the climate and occurs on a molecular scale. Accurate models exist for radiative transfer, but they are not fast enough to be run every time step in a global climate model. A potentially promising way to meet both, the requirements for speed and accuracy that is addressed in this thesis is to develop machine learning and especially deep learning technique-based radiative transfer parametrizations for climate models.

The candidate (f/m/d) will be part of an international team of the European Research Council (ERC) Synergy Grant on „Understanding and Modelling the Earth System with Machine Learning (USMILE,

Field of activity:

  • Development of machine learning and especially deep learning technique-based radiative transfer parametrizations for climate models
  • 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, computer science, data science, or similar field
  • Very good programming skills (preferably python) and experience in data analysis
  • Interest and ideally experience in climate modelling and research
  • Interest and ideally experience in ML / DL
  • 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)

Additional (desired) skills:

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

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 observations. The department develops innovative methods, including ML techniques, for the evaluation and analysis of Earth system models in comparison to observations with the aim to better understand and project climate change. The PhD student (f/m/d) 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 interaction with other researchers participating in the European Research Council (ERC) Synergy Grant on “Understanding and Modelling the Earth System with Machine Learning” (USMILE, 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 and ozone assessments of the Intergovernmental Panel on Climate Change (IPCC) and the World Meteorological Organization (WMO).

 

General hints

The University of Bremen strives to increase the number of females in science; therefore women are explicitly encouraged to apply. Applicants with a migratory background are highly welcome. In case of essentially equal personal aptitudes and qualification, disabled persons will be given priority.

Contact

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

Please send your application (cover letter, cv, and copy of your degree certificates) until the 9 December 2021 by indicating the job id A267/21 to:

University of Bremen / FB1
Secretary of Prof. Eyring
Mrs. Sandra Smit
Otto-Hahn-Allee 1
D-28359 Bremen
Germany

or by e-mail (possibly in a single PDF file): sandra.smitprotect me ?!uni-bremenprotect me ?!.de (phone: +49 421 218 62141)

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