Thesis: Advanced Modeling and Optimization Using Artificial Intelligence for Dry-Process Electrode Manufacturing (M/F)

CNRS - National Center for Scientific Research; France; On-site; EUR 19,000 - 23,000; Full time

Job summary

CNRS – Laboratoire de réactivité et de chimie des solides in France seeks a First Stage Researcher (R1) to develop a multiscale modeling framework for the dry-process electrode fabrication. You will perform numerical simulations with DEM and FEM and implement models using LIGGGHTS, LAMMPS, and COMSOL, while collaborating with partners to optimize processes through data-driven methods.

This 35-hour weekly position starts on 1 Dec 2026 and is temporary, focusing on advancing material science for

Qualifications

• Experience in numerical simulation and modeling of granular systems.
• Familiarity with DEM/FEM-based approaches and relevant software tools.
• Ability to analyze experimental data and optimize processes.

Responsibilities

• Develop a multiscale modeling framework for dry-process electrode fabrication.
• Implement models using LIGGGHTS, LAMMPS, and COMSOL Multiphysics.
• Collaborate with partners to collect, organize, and analyze data.
• Perform data-driven optimization to improve process efficiency.

Skills

Numerical simulation

Multiphysics modeling

Data analysis

Python/ MATLAB

Education

Master's degree in a related field

Tools

LIGGGHTS

LAMMPS

COMSOL Multiphysics

Finite Element Method (FEM)

Discrete Element Method (DEM)

Job description

Organisation/Company CNRS Department Laboratoire de réactivité et de chimie des solides Research Field Mathematics History » History of science Researcher Profile First Stage Researcher (R1) Application Deadline 18 Nov 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Dec 2026 Is the job funded through the EU Research Framework Programme? Horizon Europe Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The research will focus on numerical simulation and multiphysics modeling, including the development and implementation of models based on the discrete element method (DEM). These models will enable the analysis of granular dynamics, powder mixing, and particle interactions at the mesoscopic scale. In addition, models based on the finite element method (FEM) will be developed to simulate the mechanical behavior, compaction, and final structure of the electrodes at the macroscopic scale. This modeling of the various process steps will rely on expert use of the LIGGGHTS, LAMMPS, and COMSOL Multiphysics software packages.

Process optimization will be carried out in close collaboration with the research consortium. The project will involve the collection, organization, and analysis of experimental data provided by the various partners. A data-driven optimization loop will be developed to correlate numerical modeling parameters with experimental results. The ultimate goal will be to identify optimal parameter sets—such as formulation, pressure, or velocity—in order to significantly improve the efficiency of the dry process and guide the consortium's future experimental campaigns.

As part of the development of new battery technologies, the PhD student will be tasked with designing a multiscale modeling framework dedicated to the dry-process fabrication of electrodes. This work aims to simulate, understand, and optimize the physical and mechanical interactions of materials during production by combining state-of-the-art numerical simulation methods with artificial intelligence tools.