Postdoctoral researcher M/F

Organisation/Company CNRS Department Laboratoire Charles Coulomb Research Field Physics Researcher Profile First Stage Researcher (R1) Application Deadline 19 Oct 2026
- 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 2 Nov 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 2DPAM project (2D Phonons Accelerated by Machine Learning) investigates van der Waals heterostructures formed by encapsulating operative 2D materials such as graphene and transition-metal dichalcogenides with boron nitride. The project aims to understand how interlayer couplings introduced by the encapsulator affect electronic and thermal transport properties, with a focus on phonon dynamics as the key dissipation mechanism. By combining ab initio simulations with machine learning, the project seeks to reproduce, explain, and extend experimental observations of electronic mobility and electron cooling rates in encapsulated devices.

The postdoctoral researcher will develop and apply computational methods to simulate phonon dynamics in encapsulators, particularly hexagonal and rhombohedral boron nitride. The role involves calculating phonon frequencies and lifetimes as a function of temperature, studying the impact of layer thickness and isotopic disorder, and integrating these results into transport models to predict electronic and thermal properties. The researcher will use machine learning interatomic potentials to overcome computational barriers in simulating phonon-phonon interactions, and will work with coupled Boltzmann transport equations to treat electrons and phonons on equal footing. The position also includes extending existing solvers and validating models against experimental data.

The research will take place in the Statistical Physics team of the Theoretical Physics axis, at the Charles Coulomb laboratory, located on the Triolet campus of the University of Montpellier. Supervision will be provided by Thibault Sohier, with weekly meetings. The project involves close collaboration with the laboratory's experimental teams.

Candidates must hold a PhD in Physics, Materials Science, or a related field, with a strong background in computational condensed matter physics. Required skills include experience with Density Functional Theory (DFT) simulations, particularly for 2D materials and phonon calculations. Proficiency in machine learning techniques for materials science, especially machine learning interatomic potentials, is essential. The candidate should have knowledge of electron-phonon and phonon-phonon interactions, and experience with Boltzmann transport equations and their numerical solvers. Familiarity with van der Waals heterostructures, long-range Coulomb interactions, and tools such as TDEP for phonon dynamics is highly desirable. Strong programming skills in Python, Fortran, or similar languages for scientific computing and high-performance computing environments are necessary. Excellent analytical and problem-solving abilities, along with the capacity to work both independently and collaboratively, are expected.