Tenure Track Assistant Professor in Regional-scale Atmospheric Modelling
Aarhus University, Department of Environmental Science
Climate: Past, Present & Future (CL)
Energy, Resources and the Environment (ERE)
We are seeking a dedicated and ambitious researcher to strengthen regional-scale atmospheric modelling and environmental advisory capacity at Aarhus University. You will contribute to cutting-edge research on air quality and climate interactions, satellite data integration, and model development — while also playing a key role in Denmark’s science-based environmental advisory services.
In this position, you will:
- Develop and apply regionalscale atmospheric chemical transport models like the DEHM model (Danish Eulerian Hemispheric Model), and operational air quality forecast systems like CAMS (Copernicus Atmosphere Monitoring Service)
- Integrate satellite observations and data assimilation techniques into modelling workflows
- Combine numerical models with machinelearning/data-driven approaches
- Contribute to operational and advisory tasks, including:
- National environmental monitoring programmes
- Scientific support for Danish authorities and ministries
- Contributions to European services (e.g., Copernicus CAMS)
- Participate in and colead research and advisory projects, including external funding applications
- Take part in developing and enhancing interdisciplinary collaboration both internally and externally
We expect that you have:
- A PhD in meteorology, physics, geophysics, mathematical modelling, chemistry, engineering or related disciplines
- Experience with regional atmospheric chemical transport models and with satellite data and data assimilation techniques
- Programming experience (e.g., Fortran, Python, shell scripting) and HPC workflows
- Experience working with operational forecasting systems and advisory tasks
We are looking for a researcher with a strong publication record relative to their career stage and a clear interest in interdisciplinary collaboration. Ideally, you also bring experience with machine-learning or hybrid modelling approaches, as well as proposal development and external funding. We value curiosity, independence and collaboration, and we are looking for someone who thrives in an environment that combines scientific excellence with societally relevant advisory work.
Follow instructions and link to application system here: