Postdoc: First-Principles+ML for Disordered Energy Materials

Aarhus Universitet; Denmark; Hybrid; DKK 420,000 - 540,000; Full time

Job summary

Aarhus University invites applications for a 24-month postdoctoral position in computational materials chemistry, hosted by the Center for Sustainable Energy Materials (CENSEMAT). The successful candidate will be primarily stationed at TU Wien, with regular shorter research stays at Aarhus University, working on DFT, machine-learned force fields and atomistic simulations.

The project aims to uncover how correlated structural disorder governs transport and functional properties in energy

Qualifications

• PhD or submission before start date in computational materials science, physics, theoretical or computational chemistry, or related field.
• Strong experience with first-principles calculations of solids using DFT and building ab initio reference databases for training ML models.
• Experience developing, training, evaluating and validating machine-learned interatomic force fields.
• Experience with molecular dynamics and lattice-dynamical or phonon calculations for complex solids.
• Research linking local structure and defects to lattice thermal transport and ion migration in solids.
• Strong scientific programming skills and HPC workflows; Python proficiency expected.
• Publications appropriate to career stage demonstrating independent computational research.
• Fluency in written and spoken English.
• Ability to work independently and collaborate across theoretical and experimental disciplines.
• Willingness to be based primarily in Vienna with stays in Aarhus and online collaboration.

Responsibilities

• Generate and curate ab initio reference datasets for defective and disordered crystalline materials.
• Develop, train, validate and benchmark machine-learned interatomic force fields for multicomponent materials.
• Use MD and lattice-dynamical methods to study local disorder, phase stability, and transport properties.
• Investigate how defects and correlated disorder affect ion migration and thermal transport.
• Relate simulations to experimental disorder models and data from scattering, NMR and electron microscopy.
• Collaborate with TU Wien and Aarhus CENSEMAT teams; participate in shorter stays in Aarhus.
• Communicate results via publications, conferences and reproducible computational workflows.

Skills

DFT simulations

ML interatomic potentials

Molecular dynamics

Python programming

Linux HPC

English fluency

Independent research

Cross-disciplinary collaboration

Education

PhD in computational materials science/physics/chemistry

Tools

Python

ab initio databases

DFT software (VASP/Quantum ESPRESSO)

Job description

Aarhus University invites applications for a 24-month postdoctoral position in computational materials chemistry, hosted by the Center for Sustainable Energy Materials (CENSEMAT). The successful candidate will be primarily stationed at TU Wien, with regular shorter research stays at Aarhus University, working on DFT, machine-learned force fields and atomistic simulations.

The project aims to uncover how correlated structural disorder governs transport and functional properties in energy