Prof. Dr. Benedikt Soja
Prof. Dr. Benedikt Soja
Assistenzprofessor am Departement Bau, Umwelt und Geomatik
Stellvertretender Leiter Inst. f. Geodäsie und Photogrammetrie
Zusätzliche Informationen
Employment record
since 2020 Assistant Professor (Tenure Track) of Space Geodesy, ETH Zurich
2019 – 2020 Scientist II, NASA JPL, Geodynamics and Space Geodesy
2016 – 2019 Postdoctoral researcher, NASA JPL, Geodyn. and Space Geodesy
2016 – 2016 Postdoctoral researcher, GFZ Potsdam, Space Geodetic Techniques
2013 – 2016 Project assistant “VLBI analysis in real-time”, GFZ Potsdam
Education
2016 Ph.D. in “Geodesy and Geoinformation”, TU Vienna
2013 Diplom-Ingenieur (MSc) in “Geodesy and Geophysics”, TU Vienna
Selected awards
Outstanding Early Career Scientists Award of the EGU Geodesy Division (2019)
Friedrich-Robert-Helmert Prize for best Ph.D. thesis at GFZ Potsdam in 2016/2017
Stipend of Excellence (Austrian Fed. Ministry of Science, Res. and Econ., 2017)
Promotio sub auspiciis Praesidentis rei publicae (2017)
NASA Postdoctoral Program Fellow at NASA JPL (2016)
Bernd Rendel prize for Geosciences 2015 (German Research Foundation, 2015)
Selected functions
Chair of GGOS Focus Area on AI for Geodesy (since 05/2023)
Chair of the International Association of Geodesy's Inter-Commission Committee on Theory Joint Study Group “Machine learning in geodesy” (2019-2023)
Vice-chair of GGOS Focus Area on Geodetic Space Weather Research Joint Working Group: “Improved understanding of space weather events and their monitoring by satellite missions” (2019-2023)
Convener for session “Data science and machine learning in geodesy” at EGU General Assembly 2021
Co-convener for session “Ionosphere, thermosphere and space weather: monitoring and modelling” at EGU General Assemblies 2020 and 2021
Co-editor of the Special Issue “Observing and Modelling Ionosphere and Thermosphere Using In Situ and Remote Sensing Techniques” in Remote Sensing (2020)
Vorlesungsverzeichnis
Herbstsemester 2024
Nummer | Veranstaltung |
---|---|
101-0522-10L | Doctoral Seminar Data Science and Machine Learning in Civil, Env. and Geospatial Engineering |
101-0523-15L | Frontiers in Machine Learning Applied to Civil, Env. and Geospatial Engineering |
103-0187-01L | Space Geodesy |
103-0251-00L | Computational Methods for Geospatial Analysis |