PhD Fellowship in Physics-Based Models, Sophia Antipolis, France

PhD Fellowship in Physics-Based Models: Discover a challenging and promising PhD fellowship opportunity at Inria, the French national research institute for digital sciences. The program focuses on the development of physics-based multi-level surrogate models from neural networks, with a specific application to electromagnetic wave propagation. This initiative is part of the Institute’s commitment to advance digital technologies through cutting-edge research.

Doctoral scholarship for the development of physics-based multi-level surrogate models from neural networks

Designation: PhD position

Research area: Construction of physics-based multi-stage surrogate models from neural networks with a focus on the propagation of electromagnetic waves

Location: Sophia Antipolis, France

Eligibility/Qualification:

• University degree or equivalent in a relevant field
• Appreciated qualifications include a Master’s degree in Applied Mathematics or Scientific Computing
• Solid knowledge of numerical analysis for partial differential equations (PDEs)
• Knowledge of machine learning/deep learning with artificial neural networks
• Familiarity with the physics of electromagnetic wave propagation
• Basic knowledge of Python programming, TensorFlow and PyTorch
• Good command of spoken and written English

Job description:
The successful candidate will participate in a challenging research program focused on the development of physics-based multi-stage surrogate models consisting of neural networks for modeling electromagnetic wave propagation in the frequency domain. Core activities include bibliographic studies, software development, numerical evaluation of proposed models, and publications. The program offers a unique opportunity to contribute to the scientific community and gain valuable insights into the interdisciplinary field of scientific machine learning.

How to apply:
To apply for this position, please ensure you submit your application online on the Inria website before the deadline.

Last application date: 30 November 2024

This fellowship represents an exciting opportunity for motivated individuals who want to push the frontiers of scientific machine learning and make a significant impact in the field of digital technology.

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