Post-doc (M/F) – Machine Learning and Raman Spectroscopy for Materials

Job Description

Missions

The postdoctoral researcher will have the primary responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials.

Activity

The successful candidate will design and implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment, electronic properties, and spectroscopic response. A major component of the project will focus on coupling DFT calculations with machine learning models to accelerate spectral prediction, identify robust physico-chemical trends, and extract relevant structural and electronic descriptors to inform the models.
Particular attention will be paid to ensuring consistency between theoretical and experimental data, as well as to the development of a structured database generated from ab initio calculations and enriched through machine learning approaches. The objective is to develop predictive tools to analyze electrochemical evolution, reaction mechanisms, and aging phenomena at electrode-electrolyte interfaces.

Your Profil

Skills

Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field;
-demonstrate strong expertise in machine learning (regression, neural networks);
-have experience in vibrational modeling and DFT methods;
-possess solid skills in scientific programming (Python) and data processing ;
-have ability to work in an interdisciplinary environment involving both modelers and experimentalists.

Your Work Environment

This position is part of the national PEPR Batteries program. The project aims to develop innovative approaches combining ab initio modeling, Raman vibrational spectroscopy, and machine learning for the study of electrode materials and their interfaces, with a focus on operando monitoring of full battery cells.

The position is based at the Institut Charles Gerhardt Montpellier, within the Theoretical Physical Chemistry & Modeling Department, in an environment at the interface between modeling, spectroscopy, and materials science for energy applications. The work will be carried out in close collaboration with experimental researchers developing advanced Spatially Offset Raman Spectroscopy (SORS) methods.

Compensation and benefits

Compensation

from 3501€ gross per month, depending on experience

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions