academic employee
vor 2 Monaten
Job description
For our Campus Alpin in Garmisch-Partenkirchen (IMKIFU), we are looking for a position in the MERGOSAT project (merging rain rate estimates from opportunistic sensors and geostationary satellites), which is being carried out together with the group of Dr. Uwe Siart at the Technical University of Munich and Dr. Vojtech Bares is carried out at the Czech Republic Technical University in Prague.
The overarching goal of the project MERGOSAT is to develop novel methods for the generation of improved near-real-time rainfall maps for data-scarce regions in developing countries via a combination of data from geostationary satellites (GEOsat) and "opportunistic" ground-based sensors, namely commercial microwave links (CMLs) and satellite microwave links (SMLs).
Our project partners will work on improving the processing of CML and SML data using new insights from lab and simulation work as well as using information from GEOsat data to support the processing. At KIT we will develop a generative Deep Learning method for deriving improved rainfall fields from GEOsat data and from merging all data sources.
Your main tasks will be:
Development of a generative Deep Learning model to predict skillful rainfall fields from GEOsat observations with high spatial and temporal resolution Extension of the generative model to allow the integration of ground-based rainfall data from CMLs and SMLs Large-scale validation of the developed methods in developing countriesYour work will be carried out in cooperation with colleagues at NWC SAF, who are responsible for the operational processing of GEOsat data for nowcasting.
A part-time doctorate is possible. During which there are two research visits at NWC SAF in Valencia planned.
Starting date
01.11.2024
Personal qualification
You have a master degree either from the field of computer science or from natural sciences and mathematics. The actual subject is not that important. We care more about your individual skills.
Specifically, you ideally have:
strong programming skills, preferably in Python; expertise with handling large datasets, e.g. on HPC or cloud compute infrastructure; good knowledge of deep neural networks or other advanced machine learning methods; a basic understanding of hydrometeorological processes and observations; and the intuition to link data and code to real-world observations and challenges.You will also need to have a good command of written and spoken English.
Salary
Salary category 13, depending on the fulfillment of professional and personal requirements.
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