The Chair of Space Geodesy faces the challenges and profits from the opportunities that come with “big data” in geodesy. We design and implement advanced machine learning techniques to automate the processing of GNSS and VLBI data, resulting in a faster availability and higher quality of geodetic products. Also, machine learning is applied in the large-scale analysis of GNSS time series to detect and investigate geophysical signals, inter alia related to hydrological and seismic events, as well as to severe weather events and ionospheric disturbances. Finally, we improve the predictions of several types of geodetic parameters by machine learning. In particular, we focus on determining future Earth orientation parameters, station coordinates and atmospheric parameters.
The Chair of Space Geodesy invites applications for an exciting PhD opportunity focused on advancing Very Long Baseline Interferometry (VLBI) research.
On the 19th of September, Mostafa Kiani Shahvandi successfully defended his Ph.D. thesis entitled "Geodetic time series analysis and prediction using machine learning".
