You will be part of the Chair of Space Geodesy, a newly established research group around Prof. Benedikt Soja that will focus on the application of machine learning techniques to geodetic problems, including automated data processing and large-scale time series analysis. Additionally, you will benefit from collaborating with members of the Chair of Mathematical and Physical Geodesy (Prof. Rothacher) with vast experience in geodetic satellite data analysis.
We are seeking a highly motivated PhD student to work on the application of machine learning to Global Navigation Satellite Systems (GNSS) data analysis. The selected candidate will develop and implement machine learning algorithms for the refined processing of GNSS observations to estimate geodetic parameters of highest quality. Depending on the previous experience of the candidate, specific parameters, such as tropospheric, ionospheric or Earth orientation parameters, will be investigated in greater detail. Of particular interest will be the detection of patterns and anomalies in the geodetic observations and resulting products.
Applicants must hold a Master’s degree in geodesy, geosciences, physics, informatics, computational sciences or a related field. The successful candidate will have strong analytical skills as well as experience in data processing and programming. A solid background in either machine learning/data science or space geodesy would be ideal for this PhD position. A very good command of the English language is mandatory. Good interpersonal skills and the willingness to work in an international team of researchers and students are expected.