PhD Position F/M Deep Neural Network-assisted computational design of highly efficient ultrafast dynamical metasurfaces
il y a 2 heures
Context and Key Features of the Position
The present doctoral project is part of a collaborative research endeavor between the Atlantis project-team from the Inria Research Center at Université Côte d'Azur and the CNRS-CRHEA laboratory in Sophia Antipolis, France.
Atlantis is a joint project-team between Inria and the Jean-Alexandre Dieudonné Mathematics Laboratory at Université Côte d'Azur. The team comprises applied mathematicians and computational scientists who are collaboratively undertaking research activities aimed at designing, analyzing, developing, and applying innovative numerical methods for systems of partial differential equations (PDEs) modeling nanoscale light-matter interaction problems.
In this context, the team is developing the DIOGENeS software suite, which implements several Discontinuous Galerkin (DG) type methods tailored to the systems of time- and frequency-domain Maxwell equations possibly coupled to differential equations modeling the behavior of propagation media at optical frequencies.
Main Objectives of the PhD Project
The main goal of this PhD project is to use numerical methods to optimize the design of active nanostructures in order to achieve the highest possible phase modulation and amplitude response.
The optimization process will focus on adjusting the dimensions and shapes of meta-atoms and will take into account the characteristics of the active materials used.
Methodology and Expected Outcomes
The PhD project will involve the development of an Artificial Neural Network (ANN)-assisted Reduced-Order Modeling (ROM) strategy for the particular modeling context of active metasurfaces.
This will require extending the approach previously proposed in [LHLL21] 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 similar to the ones described in [MELS19, MELS21].
Key Activities and Deliverables
- Bibliography study on existing ANN-based ROM methods
- Formulation of an ANN-based ROM method for time-domain nanophotonics in the context of electrically-driven active metasurfaces
- Development (in Fortran 2003 and Python) of the method for 3D problems
- Detailed assessment of the novel ANN-based ROM method by considering model problems
- Formulation and development of an inverse design workflow that leverages the novel ANN-based ROM method
- Application of the inverse design methodology to numerical optimization of electrically-driven active metasurfaces
- Scientific publications
Required Skills and Qualifications
The successful candidate will have a strong background in numerical analysis for PDEs, machine learning, and deep learning with artificial neural networks.
They will also have basic knowledge of physics of electromagnetic wave propagation and software development skills in Python and Fortran 2003, including parallel programming with MPI and OpenMP.
Excellent communication and teamwork skills are also essential.
Benefits and Remuneration
The PhD position comes with a gross salary of 2100€ per month (years 1 & 2) and 2190€ per month (year 3).
The position also offers subsidized meals, partial reimbursement of public transport costs, and a range of other benefits.
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Technopole de Sophia Antipolis, France INRIA Temps pleinJob DescriptionContext and Key ResponsibilitiesThe PhD project is part of a collaborative research effort between INRIA and the CNRS-CRHEA laboratory in Sophia Antipolis, France. The project aims to develop novel numerical methods for the design and optimization of active metasurfaces, which are engineered materials that can control the behavior of...
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