PhD Scholarship in Physics-Based Models: Explore a challenging and promising PhD scholarship opportunity at Inria, the French national research institute dedicated to digital sciences. The program focuses on developing physics-based multilevel surrogate models from neural networks, with a specific application to electromagnetic wave propagation. This initiative forms part of the Institute’s commitment to spearheading digital technology advancements through cutting-edge research.
PhD Scholarship Opportunity in Building Physics-Based Multilevel Surrogate Models from Neural Networks
Designation: PhD Position
Research Area: Building Physics-Based Multilevel Surrogate Models from Neural Networks with an Emphasis on Electromagnetic Wave Propagation
Location: Sophia Antipolis, France
Eligibility/Qualification:
- Graduate degree or equivalent in a relevant field
- Valued qualifications include a Master’s in applied mathematics or scientific computing
- Sound knowledge of numerical analysis for partial differential equations (PDEs)
- Proficiency in machine learning/deep learning with artificial neural networks
- Familiarity with the physics of electromagnetic wave propagation
- Basic skills in Python programming, TensorFlow, and PyTorch
- Good command of spoken and written English
Job Description:
The successful candidate will engage in a rigorous research program focused on the development of physics-based multilevel surrogate models from neural networks to model electromagnetic wave propagation in the frequency domain. Core activities include bibliographical study, software development, numerical assessment of proposed models, and publications. The program offers a unique opportunity to contribute to the scientific community and gain invaluable insights into the interdisciplinary field of scientific machine learning.
How to Apply:
To apply for this position, ensure to submit your application online on the Inria website before the deadline.
Last Date for Apply: November 30, 2024
This scholarship presents an exciting opportunity for motivated individuals eager to delve into the frontier of scientific machine learning and make a substantial impact in the realm of digital technology.
