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Wave propagation phenomena are ubiquitous in our modern society and manifest themselves in various contexts such as telecommunications, imaging techniques (medical, seismic), information processing (photonics), energy harvesting and health (biomedical applications). Numerical modeling plays an increasing role in understanding complex wave propagation phenomena or for mastering the wave interactions with our environment. Many numerical methods have been designed for the numerical solution of wave PDE models for unsteady or harmonic propagation regimes, i.e., for time-domain and frequency-domain formulations of these models. In particular high order discontinuous finite element methods, named as Discontinuous Galerkin (DG) methods or Hybridized DG (HDG) have shown significant benefit in terms of accuracy and have become popular. The resulting discretized problem reduces to the solution of a large sparse linear system of equations.

The work of this postdoc will be carried out within the framework of the joint Inria Industrie Concace project team whose partners are Airbus CRT and Cerfacs, which is trilocated between Bordeaux, Issy les Moulineaux and Toulouse. In additional close interactions with other Inria teams located in various Inria centres are foreseen.


The candidate will have to:

  • Define an interface enabling the use of various boundary transfert conditions in a domain decomposition contexte
  • Implement them in the C++ package Compose designed by the Concace team
  • Validate the numerical and parallel performance for computations in lectromganetism or seismic
  • Disseminate the results through paper writing and talks in seminars/conferences

Main activities

The main activities will be:

  • Analyse the requirements
  • Propose flexible and generic solution
  • Develop a C++ interface


Technical skills and level required: 5 years or more of higher education or equivalent, master or engineering degree or PhD in applied mathematics or computer science with a scientific computing component. Knowledge of machine learning would be an asset.

Languages: the working language will be mainly French, but English will be used in exchanges with non-French speaking team members or collaborators.

Interpersonal skills: enjoy working and interacting in a collaborative research environment, curiosity and creativity.

Additional skills: writing scientific papers and public presentations of results.