University of Innsbruck, Digital Sience Center & Department of Atmospheric and Cryospheric Sciences

Homepage: https://acinn.uibk.ac.at

Lightning detection networks have monitored lightning continuously for more than three decades, but upgrades to sensors and algorithms introduce hidden artifacts that can distort long-term lightning climatologies.

This PhD project at the University of Innsbruck (Digital Science Center & Department of Atmospheric and Cryospheric Sciences) will develop statistical and machine-learning methods to identify and correct these artifacts. The reconstructed lightning climatologies will enable new analyses of convective activity and lightning variability in a changing climate, provide a better foundation for lightning risk analysis (wind turbines!), and support the evaluation of atmospheric models.

We are looking for candidates with a Master’s degree in meteorology, statistics, data science, environmental science, lightning physics, or related fields, and strong programming skills in R or Python (with willingness to work in R). We encourage candidates with strong backgrounds in statistics or machine learning to apply since prior expertise in atmospheric science is not required and can be acquired during the PhD.

➡ Full job description and application details:
[link to full announcement]