Postdoc: ML Force Fields for Disorder in Energy Materials

Natural Sciences - Institut for Kemi; Denmark; Hybrid; DKK 520,000 - 720,000; Full time

Job summary

Aarhus University invites applications for a 24-month Postdoctoral position in computational materials chemistry, focusing on first-principles modelling and ML force fields for correlated disorder in energy materials. The project spans DFT, ML interatomic potentials, and atomistic simulations, with research stays at TU Wien and Aarhus University.

The successful candidate will generate datasets, develop ML force fields, and explore transport properties, lattice dynamics, and defect effects in

Qualifications

• PhD in computational materials science, physics, chemistry, or closely related field.
• Strong experience with first-principles calculations of solids using DFT and managing ab initio databases for ML models.
• Experience in developing/training/validating ML interatomic force fields for atomistic simulations.
• Experience with molecular dynamics and lattice-dynamical or phonon calculations for disordered solids.
• Proven programming skills, Linux HPC, automated workflows, and reproducible data analysis.

Responsibilities

• Generate ab initio reference datasets for complex materials.
• Develop and benchmark ML interatomic force fields for multicomponent materials.
• Use MD and lattice-dynamical methods to study local disorder and transport.
• Investigate effects of defects and correlated disorder on ion migration and thermal transport.
• Relate simulations to experimental disorder models and data.
• Collaborate across TU Wien and Aarhus University with regular research stays.
• Publish results and contribute to reproducible computational workflows.

Skills

DFT calculations

Machine-learning interatomic force

Molecular dynamics

Linux-based HPC

Python programming

English fluency

Education

PhD in computational materials science / physics / chemistry

Tools

Python

High-performance computing

Job description

Aarhus University invites applications for a 24-month Postdoctoral position in computational materials chemistry, focusing on first-principles modelling and ML force fields for correlated disorder in energy materials. The project spans DFT, ML interatomic potentials, and atomistic simulations, with research stays at TU Wien and Aarhus University.

The successful candidate will generate datasets, develop ML force fields, and explore transport properties, lattice dynamics, and defect effects in