The candidate will join the SLR group and will investigate data analysis and automation techniques with special emphasis on the usage of Machine Learning (ML). The results are an input for orbit determination, prediction and characterization of satellites and space debris while improving station performance and precision.
Besides SLR measurements, the group performs space debris laser ranging (SDLR) and single photon light curves (LC) measurements, characterizing sunlight reflections of space objects. The IWF can thereby rely on historical data dating back more than 20 years. Special emphasis of the candidate will be put on the utilization of machine learning related to new observation technologies (e.g. MHz laser ranging), the combined usage of data from different observation sources (data fusion) and the modernization of the SLR station. The candidate with work in close cooperation with IWF’s machine learning experts.