We are hiring! (Postdoc position)

We are currently looking for a 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. Here, our goal would be 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, our goal would be to 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 are currently not resolved in global climate models (e.g. clouds), or processes that are highly computationally expensive on larger - but spatially well-resolved - scales (e.g. atmospheric chemistry).
• Cloud feedback and forcing. Clouds are the major uncertainty factor in global warming projections under increased atmospheric carbon dioxide levels. Here, the project goals would be aligned with 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 and, thus, climate sensitivity.
• Stratospheric composition. Changes in stratospheric composition are of fundamental importance for life on Earth, for example by impacting the ozone layer that protects us from harmful solar ultraviolet radiation. As part of this project, our goal would be to derive observational constraints on, and to develop a novel understanding of, future changes in stratospheric composition. As such, your project would link to our recent work on the ozone layer and on 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, our goal would be to 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 chemistry modelling.

The project can be flexibly adjusted to candidate interests. We in particular welcome candidates interested in machine learning method development and its innovative applications.

The position will be directly associated with the Faculty of Informatics at KIT. The candidate will participate in teaching activities of the Chair for AI in Climate and Environmental Sciences, will present at (inter-)national conferences, and 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 in general 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 deadline on 11th June 2023. For questions concerning the position itself, please contact Prof. Dr. Peer Nowack (peer.nowack@kit.edu).