Post-doctoral fellow IRL Georgia Tech - CNRS in Data-Driven Grain Boundary Engineering M/F

Job Description

Missions

This project aims to develop a data-driven Process-Structure-Mechanisms-Properties (PSMP) framework to quantitatively analyze the grain boundary structure during motion and link it to kinetic properties. To do this, the researcher will use thousands of molecular dynamics (MD) simulations, topological data analysis (persistent homology and pair correlation functions), and principal component analysis.
The objectives of this project are:
- Develop a low-dimensional structural time-series (GBSTF) to quantify the atomic structure
- Correlate dynamic structural trajectories with different migration kinetics
- Discriminate between migration mechanisms activated by different driving forces (mechanical stresses or jumps in chemical potential)
- Provide a quantitative tool for predicting and controlling microstructure evolution

Activity

This project aims to develop a data-driven Process-Structure-Mechanisms-Properties (PSMP) framework to quantitatively analyze the grain boundary structure during motion and link it to kinetic properties. To do this, the researcher will use thousands of molecular dynamics (MD) simulations, topological data analysis (persistent homology and pair correlation functions), and principal component analysis.
The objectives of this project are:
- Develop a low-dimensional structural time-series (GBSTF) to quantify the atomic structure
- Correlate dynamic structural trajectories with different migration kinetics
- Discriminate between migration mechanisms activated by different driving forces (mechanical stresses or jumps in chemical potential)
- Provide a quantitative tool for predicting and controlling microstructure evolution

Your Profil

Skills

Expertise en science des matériaux computationnelle et en simulations de dynamique moléculaire (MD). Maîtrise de l'application d'outils avancés d'analyse topologique des données, plus particulièrement l'homologie persistante (PH) et les fonctions de corrélation de paires (PCF). Capacité à effectuer une réduction de dimensionnalité en utilisant l'analyse en composantes principales (PCA) pour extraire les fonctions de transition de structure.

Your Work Environment

This research will be carried out in the context of IRL 2958 GT-CNRS, a joint international laboratory between the Georgia Institute of Technology and CNRS, under the direction of Prof. L.A. Barrales-Mora, a specialist in computational materials science. Like any research project, this project is a collaboration with a set of international partners from different fields and will therefore require a certain amount of collaborative work and communication.The position is in a sector subject to the Protection of Scientific and Technical Potential (PPST) regulations; therefore, in accordance with these regulations, your appointment must be approved by the relevant authority at the Ministry of Higher Education and Research (MESR).

Constraints and risks

Working in a bilingual environment

Compensation and benefits

Compensation

3041.58€ 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