PhD Position F/M Deep Neural Network-assisted Computational Design of Highly Efficient Ultrafast Dynamical Metasurfaces
il y a 4 semaines
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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