PhD Position F/M Deep Neural Network-assisted Computational Design of Highly Efficient Ultrafast Dynamical Metasurfaces

il y a 4 semaines


Technopole de Sophia Antipolis, France INRIA Temps plein

Context and Skills

The research project is part of a collaborative project between INRIA and the CNRS-CRHEA laboratory in Sophia Antipolis, France. The team is developing a unique numerical framework leveraging the capabilities of Discontinuous Galerkin techniques for the simulation of multiscale problems relevant to nanophotonics and nanoplasmonics.

Research Goals and Objectives

  • Design and optimize active nanostructures to achieve high phase modulation and amplitude response.
  • Investigate and develop an ANN-assisted ROM strategy for the modeling context of active metasurfaces.
  • Extend the approach previously proposed by addressing the specificities of electrically-driven active metasurfaces and the efficient integration of the developed ANN-based ROM strategy in an inverse design workflow.

Main Activities

  • Studying existing ANN-based ROM methods.
  • Formulating an ANN-based ROM method for time-domain nanophotonics in the context of electrically-driven active metasurfaces.
  • Developing the method in Fortran 2003 and Python for 3D problems.
  • Assessing the novel ANN-based ROM method by considering model problems.
  • Formulating and developing an inverse design workflow that leverages the novel ANN-based ROM method.
  • Applying the inverse design methodology to numerical optimization of electrically-driven active metasurfaces.

Required Skills and Knowledge

  • Sound knowledge of numerical analysis for PDEs.
  • Sound knowledge of Machine Learning / Deep Learning with Artificial Neural Networks.
  • Basic knowledge of physics of electromagnetic wave propagation.

Software Development Skills

  • Python and Fortran 2003.
  • Parallel programming with MPI and OpenMP.

Relational Skills

  • Team worker with verbal communication, active listening, motivation, and commitment.


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