We are hiring! (postdoctoral researcher)

We are currently looking to expand our team by a new postdoctoral researcher in machine learning for climate science or machine learning for atmospheric chemistry.

The position comes with great flexibility in terms of scientific focus and project duration. Typically, we would aim for an employment duration between 2 and 4 years. Possible research areas include:

  • Regional climate change. Our goal is to develop machine learning approaches to observationally constrain the still substantial uncertainty in regional climate change projections, including changes in extreme events. Of particular interest for us are future changes in regional temperature and precipitation patterns as well as changes in heatwaves, droughts, and wildfires.
  • Parameterizations for Earth system modelling. Here, you would work on machine learning parameterizations to improve and accelerate state-of-the-art Earth System Models (ESMs); in particular the German ICON-ART model. We would either target small-scale processes that can not currently be resolved in global climate models (e.g. clouds), or processes that are too computationally expensive even on larger scales (e.g. atmospheric chemistry).
  • Cloud feedbacks and forcings. Clouds are the major uncertainty factor in global warming projections under increased atmospheric carbon dioxide levels. Here, you would join our international ML4CLOUDS project composed of researchers at KIT, the University of East Anglia, Imperial College London, the University of Oxford, and UC San Diego. Together, we are using machine learning and satellite data to derive observational constraints on potential future changes in Earth's cloud cover.
  • Stratospheric composition. Changes in stratospheric composition are of fundamental importance for life on Earth, for example by impacting the ozone layer that absorbs harmful solar ultraviolet radiation. As part of this project, you would derive observational constraints on, and develop a novel path to scientific understanding of, future changes in stratospheric composition or dynamics. As such, your project would link to our recent work on the ozone layer or on climate-driven changes in stratospheric water vapour under global warming.
  • Air pollution. Air pollution from particulates, nitrogen dioxide, and ozone remains the largest environmental health risk in Europe. Here, you would exploit the power of machine learning to substantially improve current modelling capabilities of air pollution and its extremes, thus directly addressing longstanding challenges in atmospheric composition modelling.

Candidates are invited to propose alternative topics. Most important is experience in machine learning and, ideally, in one of the scientific application areas.

The position will be directly associated with the Chair for AI in Climate and Environmental Sciences (Department of Computer Science) at KIT and will offer opportunities to gain teaching experience and to present at (inter-)national conferences. In addition, you will have opportunities to work closely with climate science researchers at KIT and collaborating groups abroad. Remuneration will be according to the E13 salary spine point for postdoctoral research positions at KIT. International candidates are eligible and are welcome to apply.

Interested? Then please send

  1. a max. one-page motivation letter, including a statement on possible topics of interest,
  2. your CV (with publication list),
  3. digital copies of major certificates (PhD, MSc/BSc),

by email attachment to Astrid Hopprich (astrid hopprich does-not-exist.kit edu). We will constantly monitor applications until the application deadline on 19th May 2023.

For questions concerning the position itself, please contact Prof. Dr. Peer Nowack (peer nowack does-not-exist.kit edu).