Phd Position F/m Reduced-order Modeling and Global
il y a 6 jours
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:CDD
**Niveau de diplôme exigé **:Bac + 5 ou équivalent
**Fonction **:Doctorant
**A propos du centre ou de la direction fonctionnelle**:
The Inria centre at Université Côte d'Azur includes 37 research teams and 8 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM...), but also with the regiona economic players.
With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.
**Contexte et atouts du poste**:
**Context and scientific environment of the project.**
This PhD project is part of a collaborative multidisciplinary project between the Atlantis project-team from Inria Research Center at Université Côte d'Azur and Institut Pascal at Université Clermont Auvergne, in the context of the ANR SWAG-P project that has started in January 2024 and that is funding the PhD.
The Atlantis project-team gathers researchers in numerical mathematics and computational physics, with an interdisciplinary focus. The team has developed a specific expertise in the efficient numerical modeling of propagation of electromagnetic wave in complex media with a strong emphasis on nanoscale light-matter interactions. Through the years, the Atlantis team has developed a strong expertise in the design, analysis and development of dedicated efficient numerical methods (based on high order accurate Discontinuous Galerkin finite elements methods). More recently, the team has also acquired a know-how of numerical optimization using various techniques, and a solid experience on high performance computing practices (parallel numerical algorithms and parallelization strategies for large-scale problems). This materializes concretely through the DIOGENeS software suite [Diog] that has already proven its crucial efficiency in nanophotonics. DIOGENeS will be the corner stone to numerically address the various complex scenarios in this PhD project.
**Mission confiée**:
**Description of the PhD project.**
To achieve this objective, it is thus of high importance to be able to characterize and optimize the optical response of single and multiple such resonators. Moreover, due to their high sensitivity, it is in particular essential to study the influence of any environment or geometrical change. In addition, the (possibly costly and difficult) use of direct experiments to address this problem, numerical methods are of high importance and provide essential support in this characterization step. Providing accurate and efficient numerical simulations in this context is highly challenging and requires robust discretization strategies and algorithms. The PhD project is part of this overall picture and will focus on the following aspects.
On the one hand, one objective of the PhD project is to carry out a comprehensive numerical study of the sensitivity of the optical response of a given Gap Plasmon Resonator (GPR) to _e.g._ variation of optical indices and geometrical parameters (metal layer width, spacer size, cubes sizes, rounding of the corners of the cubes, etc.). To achieve this goal, Uncertainty Quantification (UQ) from the perspective of robust optimization techniques will be used [El:21b], by building on and extend the pre-existing Bayesian optimization tools implemented in the DIOGENeS software tool.
On the other hand, prohibitive computational time of direct simulations of arrangement of a large number of cubes is unavoidable. It motivates the development of a smart strategy for fast characterization of the realistic GPR-based biosensors; this will be another challenging objective of the PhD project. The recent achievements (obtained in another context, see _e.g._ [Pi:24]) using non-linear reduced order model techniques based on artificial neural networks (ANN) will be the starting point in order to build an innovative reduced order modeling methodology able to quickly map the parameters of interests of a GPR-based biosensor (optical indices, typical sizes and geometries, illumination frequency, etc.) to its optical response (cross section, phase profile, field intensity, steepness and position of the Fano profile).
**Principales activités**:
**Project progress**
[El:21a] Elsawy, M. M., Gourdin, A., Binois, M., Duvigneau, R., Felbacq, D., Khadir, S., P. Genevet, Lanteri, S., _Multiobjective statistical learning optimiz
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