POST-DOC Data-driven discovery of constitutive models for granular materials from physics and in-operando intruder experiments (M/F)

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

Organisation/Company CNRS Department Sols, solides, structures, risques Research Field Engineering Physics » Acoustics Engineering » Materials engineering Researcher Profile Recognised Researcher (R2) Application Deadline 30 Oct 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jan 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The postdoctoral researcher will be responsible for designing and implementing physics-informed machine learning strategies for identifying constitutive laws in granular media. This includes the development of thermodynamically consistent models and their integration into numerical simulation tools coupled with boundary value problem solvers (e.g. finite element-based frameworks).
A significant part of the work will focus on exploiting and enriching an existing experimental database. The candidate will contribute to the design, execution, and analysis of in operando intrusion experiments, including the acquisition and processing of both macroscopic measurements (forces, torques) and internal fields obtained via tomographic imaging.
The postdoctoral researcher will also be responsible for validating and evaluating the developed models, assessing their robustness and extrapolation capabilities on unseen configurations. They will contribute to the physical interpretation of results, particularly regarding localisation mechanisms in granular materials.
Finally, the candidate will participate in the dissemination of results through scientific publications, presentations at international conferences, and interactions with academic and industrial partners.

Main activities:

• Develop constitutive models for granular media using physics-informed Machine Learning — high responsibility
• Implement a coupled learning / boundary value problem solver framework (FEM) — high responsibility
• Exploit and enrich experimental databases (forces, torques, tomography) — high responsibility
• Participate in and analyse in operando intrusion experiments — medium to high responsibility
• Validate models and assess their extrapolation capabilities — high responsibility

Secondary activities:

• Analyse localisation mechanisms — medium responsibility
• Contribute to scientific publications and communications — medium responsibility
• Collaborate with partners and provide occasional supervision — limited responsibility

Laboratoire 3SR (Soils, Solids, Structures, Risks, UMR 5521) is a joint research unit affiliated with Université Grenoble Alpes, Grenoble INP-UGA, and the Centre national de la recherche scientifique (CNRS). It is located on the Saint-Martin-d'Hères campus (Grenoble), with offices and experimental facilities distributed across several buildings (including the Galilée building and experimental halls), particularly following the 2020 fire.
Its research focuses on the mechanics of solids, materials, and structures, with applications in civil engineering, energy, environment, transport, and health. The unit develops multi-scale and multi-physics approaches (thermo-hydro-mechanical couplings, fluid/solid interactions), combining experimentation, theoretical modelling, and numerical simulation. It mainly investigates geomaterials (soils, rocks, concrete), porous or divided media (fibres, biological and bio-based materials), as well as construction materials (wood, earth, composites, ceramics).
The unit comprises 130 staff members, including 56 permanent staff (faculty members, researchers, and support staff). It is strongly integrated into local, national, and international scientific networks related to mechanics, materials, and risk analysis.
The post-doctoral researcher will be hosted within the laboratory and integrated into the GéoMécanique research team. In terms of hierarchy, the postdoctoral fellow will report scientifically to the project leader, Mr. Gioacchino Viggiani, Professor at Université Grenoble Alpes, and will also be under the administrative authority of the laboratory director, in accordance with the governance structure of Laboratoire 3SR. He/she will work in close interaction with permanent staff, PhD students, and engineers of the laboratory, as well as with experimental and modelling partners involved in the project.

-Technical skills:

• Continuum mechanics / granular media mechanics — good to expert level
• Numerical modelling (finite elements or equivalent) — good level
• Machine Learning applied to physics — good to confirmed level
• Materials thermodynamics — good level
• Experimental data analysis (forces, fields, imaging) — good level
• Scientific programming (Python/C++ or equivalent) — good level

Technical know-how:

• Coupling physical models with experimental data — expected proficiency
• Model validation and testing on new datasets — good level
• Scientific writing and communication — good level

Soft skills:

• Scientific rigor and autonomy — high level expected
• Ability to work in a multidisciplinary team — high level expected
• Curiosity and interdisciplinary openness — high level expected