GFZ is Germany’s national centre for solid Earth research. We advance the understanding of dynamic processes to address global challenges, from mitigating the impacts of natural hazards and sustaining our habitat amid global change to responsibly managing georesources. We are part of the Helmholtz Association, the largest German scientific organisation. With around 1,200 employees as well as ca. 500 guest researchers, we contribute to the Helmholtz Research Field Earth and Environment, aligning cutting-edge research with societal relevance and international collaboration. Our work integrates multidisciplinary studies across Earth’s system components, leveraging advanced technologies and infrastructure to research solutions and to transfer our knowledge to society. We are doing this according to our vision: “Taking the pulse of our Earth to safeguard a habitable planet”.
For section 4.5 Subsurface process Modelling (department “Geosystems”), we are looking for a:

YOUR QUALIFICATIONS:

• A Master’s degree (or equivalent) in geosciences, geophysics, computational sciences or related subjects
• Experience in the fields of geophysical data analysis, rock physical properties and numerical process simulations
• Provable programming skills in Python/C++
• Capacity for interdisciplinary and international teamwork and excellent communication skills
• Very good English language skills
  Start date: 1st November 2025
  Fixed-term: 36 months
  Salary: The position is classed as salary group 13 according to “TVöD Bund (Tarifgebiet Ost)”. The salary group is determined on the basis of the Collective Wage Agreement and the respective personal qualifications.
  Working hours: Part-time 75% (currently 29.25 h/week)
  Place of work: Potsdam

• Data and model preparation: Gather and revise existing 3D models, petrophysical data, borehole measurements, operational and monitoring data to build a DT (Digital Twin) of the exploration field “Lionheart”
• Conduct THM simulations and perform history matching and further testing of production scenarios for geothermal doublets
• AI-interpretable assisted surrogate modelling for sensitivity and uncertainty analyses
• Presentation of the results at conferences and publication in peer-reviewed journals