Summary
Phase transformations in metal materials, crucial for their properties, are typically modeled using phase-field models and solved numerically. Recent developments in artificial intelligence, particularly PINNS (Physics Informed Neural Networks) and Neural Operators, aim to revolutionize this modeling by providing faster solution methods for partial differential equations (PDEs). The PhD thesis aims to develop neural network approaches for phase transformation models in materials science.This PhD offer is a collaborative interdisciplinary project of IJL and IECL , crossing mathematics and materials science, and is provided by the ENACT AI Cluster and its partners.
Requirements
The funding is open to excellent students from physics, applied mathematics, engineering or other disciplines. We are looking for candidates with:
- Master’s degree in a relevant discipline.
- Solid background in numerical methods and machine learning.
- Good computer programming skills.
- Proficiency in technical report writing and presentation.
- Excellent capacity for teamwork, sense of initiative, and ability to work in a multidisciplinary
- environment.
- Fluent in English, some knowledge of French is beneficial
How to apply
To apply, send us a short statement of your interests, your CV, and full academic transcripts of the last two years of your master’s studies. The application deadline is 20 April 2025.Find more detailed information in the attached PDF document.
