Research Associate for the Project “Seismic Imaging and Machine Learning for Geothermal Reservoirs” in the pro-ject RUBADO: Geothermal Reservoir Monitoring and Seis-mic Imaging with Dark Fibers in the Upper Rhine Graben § 28 Subsection 3 HmbHG

Duties include academic services in the project named above. Research associates may also pursue independent research and further academic qualifications.

The position is part of the project “RUBADO: Geothermal Reservoir Monitoring and Seismic Imaging with Dark Fibers in the Upper Rhine Graben”, funded by the Federal Ministry for Economic Affairs and Energy of Germany (BMWE). The project is a collaboration between the University of Hamburg, the Karlsruhe Institute of Technology and Geotomographie GmbH. RUBADO investigates the innovative use of seismic sensing networks and distributed acoustic sensing (DAS) on existing urban and rural fiber-optic infrastructure for the high-resolution monitoring and imaging of geothermal reservoirs. The project aims to advance cost-effective, minimally invasive, and scalable techniques for subsurface characterization, reservoir safety, and long-term environmental stewardship in geothermally active regions. RUBADO focuses on existing geothermal infrastructure in the Upper Rhine Graben, leveraging extensive telecom “dark fiber” networks to set new standards in geoscientific imaging and monitoring, supporting the sustainable development and societal acceptance of geothermal energy solutions.

The position will be situated in the seismology group at the Institute of Geophysics of the University of Hamburg. The group specializes in ambient noise seismology and innovative seismic instrumentation, with research focusing on the understanding of seismic noise sources, imaging, and monitoring of structural changes in Earth materials. Advanced sensors and machine learning techniques are applied to investigate the near subsurface.

The successful candidate will lead efforts in seismic imaging of the shallow subsurface using ambient seismic noise and will calibrate distributed acoustic sensing measurement points. They will support the deployment of DAS and classical seismic sensors in the field and aid the development of machine learning models for event detection and classification. Responsibilities include analyzing large-scale DAS data sets, comparing results with classical sensor deployments, developing and verifying methodologies for noise-based crustal imaging in the geothermal context, and calibrating measurement points using ambient noise data. The candidate will also have the opportunity to help supervise and mentor an upcoming PhD candidate within the project.

The successful candidate can become a member of the School of Integrated Climate and Earth System Sciences (SICSS), which provides academic and soft skills training, as well as career planning support. They will participate in national/international conferences and regular project meetings with the RUBADO consortium.