Postdoctoral position on Development of supervised machine learning tools to simulate the evolution of anisotropy in the Earth's mantle at Geosciences Montpellier, France

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In the frame of the ERC RhEoVOLUTION, we are offering a fully-funded 1-year renewable (up to 3 years) postdoctoral position on Development of supervised machine learning tools to simulate the evolution of anisotropy in the Earth’s mantle at Geosciences Montpellier (CNRS & Univ. Montpellier, France).

The aim of this postdoctoral project is to develop an effective supervised machine-learning approach for accelerating the prediction of the evolution of mechanical (elastic and viscoplastic) anisotropy associated with crystal preferred orientations in geodynamic models.

In practice, the postdoctoral fellow will:

  • Acquire a solid understanding of the evolution of mechanical anisotropy during the viscoplastic deformation of the Earth’s mantle rocks and of the existing methods for its simulation (senior researchers in the RhEoVOLUTION team are experts on the topic);
  • Establish the best strategy for creating synthetic databases using the codes available within the RhEoVOLUTION team to simulate the evolution of crystallographic orientations and elastic and viscoplastic anisotropies of polycrystalline materials;
  • Define the machine learning (ML) approaches best suited to the problem (preliminary work by the RhEoVOLUTION team using LSTM networks to simulate the evolution of elastic anisotropy produced promising results);
  • Train and test the ML algorithms and perform error analysis;
  • Implement the most successful algorithms as surrogates within geodynamic simulation codes.

Extension of this approach to the study of ice flows (in collaboration with researchers from the ERC RhEoVOLUTION team in Grenoble) is envisaged. Applications in Material Sciences, in particular metallurgy, are also possible.

We are looking for highly motivated candidates with strong numerical and methodological skills. A PhD degree in Geophysics, Material Sciences, Mechanics, Applied Mathematics, Computer Sciences or a closely related field is required at the time of appointment.
Required skills:
• Proven expertise in numerical modelling and excellent programming skills (ideally Python and Fortran)
• Knowledge of solid or fluid mechanics
• Background in Machine Learning
• Ability and desire to work in a closely cooperating team but also independently
• Proficiency in English and demonstrable communication skills
Experience with Deep Learning algorithms for regression tasks, ideally using time-series data and expertise in crystal plasticity, is not essential, but will be highly valued.

Starting date: The position is open from January 1st, 2023

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