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Candidate profile

The ideal candidate is passionate about science, has a solid background in applied mathematics, statistics and physics, and has strong scientific writing skills. An experience in computer programming is required. A background or a proven interest in geoscience is appreciated. Candidates should hold a MSc in (quantitative) Earth Sciences, Geophysics or Physics, Computer Science, Geostatistics, Porous Media, Applied Mathematics, or related fields. A strong command of English language is required. French language is preferable, but not necessary.

How to apply

Application files must be sent to This email address is being protected from spambots. You need JavaScript enabled to view it. before the deadline (see PhD offers below) and must include:

  • A cover letter, 
  • A CV, including contact information for two or more referees
  • A research outcome (Master’s thesis or paper) written by the candidate
  • A transcript of grades Location

PhD topic: Stochastic inversion of FWI images for reducing structural uncertainties

Due to the lack and incompleteness of subsurface data, significant uncertainties exist on the position of structural surfaces (faults and horizons). The present project aims atdevelopingan innovative way of inverting seismic data to reduce uncertainties on these structures. Instead of using seismic recordings and wave propagation simulations, the method will rely on full waveform inversion (FWI) images and the homogenization operator(e.g., Hedjazian et al., 2021). To this end, the development of efficient computing technology for geomodeling will be necessary. This PhD will be advised by Paul Cupillard and Guillaume Caumon. Timing: The position is open to start between Sept and Dec 2022.
Deadline for applications : May 31, 2022

PhD topic: Graph machine learning for geological fault interpretation

Graph machine learning (GML) is an important recent area to analyze and forecast the behavior of complex networks. The overall goal of this PhD project is to investigate on the use of GML to associate sparse geological faults observations. This problem has recently been formulated using graph models and relatively simple association rules (Godefroy et al., 2021). In this project, our goal will be to conceptualize and test various GML strategies to solve the association problem from analog structural models or incomplete observations. One particular focus will be put on effectively assessing the higher-order association likelihoods of several observations. This PhD will be advised by Guillaume Caumon and Radu Stoica. Timing: The position is open to start between Sept and Dec 2022.
Deadline for applications : May 31, 2022

PhD topic: Unstructured mesh updating: fault editing and it impact on CO2 sequestration forecasting

Preliminary studies have shown that geological faults can play a critical role when forecasting the fate of CO2 injected in subsurface reservoirs and the mechanical hazards associated to the injection (Shao et al., 2021; Zhao & Jha, 2019). However, predicting the fault geometry from the available subsurface data is not always doable, so testing various scenarios is important to mitigate the risks. As building a first simulation mesh can be time consuming, the goal of this PhD it to develop new mesh updating approaches to rapidly insert or edit a fault in an existing mesh. For this, we will extend the implicit surface approach (Legentil et al., 2022) and test the impact of the editing on the simulation of CO2 injection. This PhD will be advised by Guillaume Caumon. Timing: the PhD is expected to start after Sept 2023, but some aspects could be addressed in the frame of a M2 internship in 2022-2023.
Deadline for applications : May 31, 2022

PhD topic: Assisted borehole interpretation and multi-wll stratigraphic correlation for geothermal modeling