Postdoc: Physics-Informed ML for Granular Materials

CNRS - National Center for Scientific Research; France; On-site; EUR 38,000 - 47,000; Full time

Job summary

The postdoctoral researcher at CNRS, Laboratoire 3SR in Grenoble, will design and implement physics-informed machine learning strategies to identify constitutive laws in granular media and integrate them into FEM-based simulations.

They will exploit and enrich experimental databases, lead in operando intrusion experiments, validate models, and contribute to publications and international conferences, working with engineers, PhD students and external partners.

Responsibilities

• Develop constitutive models for granular media using physics-informed Machine Learning.
• Implement a coupled learning / boundary value problem solver framework (FEM).
• Exploit and enrich experimental databases (forces, torques, tomography).
• Participate in and analyse in operando intrusion experiments.
• Validate models and assess their extrapolation capabilities.
• Analyse localisation mechanisms
• Contribute to scientific publications and communications
• Collaborate with partners and provide occasional supervision

Skills

Continuum mechanics / granular media

Numerical modelling

Machine Learning applied to physics

Materials thermodynamics

Experimental data analysis

Scientific programming

Tools

Python

C++

Job description

The postdoctoral researcher at CNRS, Laboratoire 3SR in Grenoble, will design and implement physics-informed machine learning strategies to identify constitutive laws in granular media and integrate them into FEM-based simulations.

They will exploit and enrich experimental databases, lead in operando intrusion experiments, validate models, and contribute to publications and international conferences, working with engineers, PhD students and external partners.