PhD Position F/M Deep Neural Network-assisted computational design of highly efficient ultrafast dynamical metasurfaces
il y a 2 jours
Context and Advantages of the Position
The present doctoral project is part of a collaborative project 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 gathers applied mathematicians and computational scientists who are collaboratively undertaking research activities aiming at the design, analysis, development and application of innovative numerical methods for systems of partial differential equations (PDEs) modelling 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 behaviour of propagation media at optical frequencies.
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.
Research Objectives
- 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
Technical skills and level required
- 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 (verbal communication, active listening, motivation and commitment)
Other valued appreciated: good level of spoken and written English
EU citizenship is mandatory
Benefits
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Contribution to mutual insurance (subject to conditions)
Salary
Gross Salary per month: 2100€ gross per month (year 1 & 2) and 2190€ gross per month (year 3)
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