Stefan Jetschny
Senior Scientist
+47 63 89 80 35
+47 991 13 513
Digital Technologies
Kjeller
sjet@nilu.no
Stefan Jetschny holds a Ph.D. degree in geophysics from the Karlsruhe Institute of Technology, Germany. He combines more than 20 years of experience both in academic and in industry research. His main research activities incorporate application of massive, scalable, numeric algorithms to large datasets to solve applied problems, earlier in the field of subsurface earth characterization and now environmental research.
During his occupation as an assistant professor, he was contributing to and managing projects with respect to seismic modeling and inversion for near surface applications. This also included the development of open source forward and inverse finite difference modeling codes. While working for a technology driven oil and gas service provider, he developed innovative and novel marine survey geometries and strategies by exploring the full chain of geological model building, synthetic data generation and application of state-of-the-art seismic processing algorithms.
In his current role as a Machine Learning Scientist / Senior researcher at NILU, Stefan contributes to the NILU’s aspiring data science team to help apply machine leaning and AI related methods to the institute’s versatile project catalog. He is actively involved in the European Topic Center for Data integration and digitalisation (ETC DI) and coordinates the EU Horizon project FAIRiCUBE. In addition, he is the main safety representative at NILU and enjoys the voluntary work in the welfare committee.
Transformative interaction between digital technologies and people for a sustainable indoor climate in schools (DIGG-MIN-SKOLE)
A good indoor environment at school is important for the health and well-being of pupils and staff, and has a significant impact on pupils' learning outcomes. Good maintenance of buildings […]
F.A.I.R. information cube (FAIRiCUBE)
The core objective of FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and […]
Stefan Jetschny holds a Ph.D. degree in geophysics from the Karlsruhe Institute of Technology, Germany. He combines more than 20 years of experience both in academic and in industry research. His main research activities incorporate application of massive, scalable, numeric algorithms to large datasets to solve applied problems, earlier in the field of subsurface earth characterization and now environmental research.
During his occupation as an assistant professor, he was contributing to and managing projects with respect to seismic modeling and inversion for near surface applications. This also included the development of open source forward and inverse finite difference modeling codes. While working for a technology driven oil and gas service provider, he developed innovative and novel marine survey geometries and strategies by exploring the full chain of geological model building, synthetic data generation and application of state-of-the-art seismic processing algorithms.
In his current role as a Machine Learning Scientist / Senior researcher at NILU, Stefan contributes to the NILU’s aspiring data science team to help apply machine leaning and AI related methods to the institute’s versatile project catalog. He is actively involved in the European Topic Center for Data integration and digitalisation (ETC DI) and coordinates the EU Horizon project FAIRiCUBE. In addition, he is the main safety representative at NILU and enjoys the voluntary work in the welfare committee.
Transformative interaction between digital technologies and people for a sustainable indoor climate in schools (DIGG-MIN-SKOLE)
A good indoor environment at school is important for the health and well-being of pupils and staff, and has a significant impact on pupils' learning outcomes. Good maintenance of buildings […]
F.A.I.R. information cube (FAIRiCUBE)
The core objective of FAIRiCUBE is to enable players from beyond classic Earth Observation (EO) domains to provide, access, process, and share gridded data and algorithms in a FAIR and […]