Postdoctoral Researcher For Data Analysis

Utrecht University’s Department of Earth Sciences is recruiting a motivated postdoctoral researcher to contribute to an interdisciplinary project funded by the Dutch Research Council (NWO). The project, titled ‘Developing a data-constrained forecasting model of surface deformation in the Groningen gas field region’, aims to quantify the physical processes driving land subsidence above the Groningen gas reservoir. Subsidence poses significant risks to the environment, buildings, infrastructure, and water management in the Netherlands, and accurate forecasting is essential for mitigation and adaptation strategies.

The successful candidate will join a team that already includes two PhD students: one developing physical compaction models and estimating mechanical properties from downhole time series, and the other deriving deformation time series from InSAR observations. The postdoctoral researcher will act as a bridge between these efforts, employing data assimilation methods to integrate InSAR, GNSS, and geodetic levelling observations with the physical models. This involves sampling model parameters and states, assessing uncertainties, and improving the models’ ability to forecast future subsidence under various scenarios such as reservoir compaction, waterboard management decisions, land use changes, and climate change.

The position is fully funded for a maximum of 36 months, offering a competitive gross salary ranging from €4,075 to €4,728 per month (scale 10.3–10.7 of the Collective Labour Agreement for Dutch Universities), inclusive of 8% holiday pay and an 8.3% year‑end bonus. Additional benefits include a pension scheme, partially paid parental leave, flexible employment terms, and access to Utrecht University’s professional development programs, sports, and cultural activities. The appointment is on-site, with the researcher based primarily at Utrecht University under the supervision of Prof. Rob Govers, while collaborating closely with partners at Delft University of Technology.

Applicants must hold a PhD in a relevant discipline (e.g., geodesy, seismology, geophysics, computer science, applied mathematics, or a related physical science) and have 0–4 years of post‑doctoral experience. Essential skills include proficiency in mathematics, statistics, and computer science, particularly Bayesian regression and large‑scale InSAR data processing. A background in physical sciences is expected, though the project is not seeking experts in machine learning or artificial intelligence. Candidates should demonstrate the ability to work independently on complex computational problems while contributing effectively within a multidisciplinary team, and must possess strong spoken and written English for academic communication.

Applications are submitted exclusively through Utrecht University’s online application system. Required documents include a letter of motivation detailing the candidate’s fit for the project and interest in Utrecht University, a comprehensive CV with research experience, publications, and relevant work, and contact information for at least two referees. Reference letters are not required at the initial stage; referees will be contacted only after the applicant is shortlisted and informed in advance. The selection process aims to conclude shortly after the application deadline of 15 July 2026, with a preferred start date of 1 September 2026 or as soon as practicable thereafter. International candidates requiring a visa or work permit should note that processing may take at least four months, and assistance is available via the university’s International Service Desk.

For further information about the position, candidates may contact Dr. Rob Govers at [email protected]. Additional details are available on the AcademicTransfer job posting (https://www.academictransfer.com/en/jobs/361698/postdoctoral-researcher-for-data…) and the university’s website. Utrecht University is committed to fostering a diverse, inclusive, and safe working environment, encouraging applications from