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

Organisation/Company CNRS Department Institut Charles Gerhardt Montpellier Research Field Chemistry Chemistry » Computational chemistry Researcher Profile First Stage Researcher (R1) Application Deadline 20 Mar 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Sep 2026 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 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.

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.

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.

This 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.

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.