Postdoc in first-principles modelling and machine-learned force fields for correlated disorder in energy materials Full-time

Postdoc In First-Principles Modelling And Machine-Learned Force Fields For Correlated Disorder In Energy Materials

The Center for Sustainable Energy Materials (CENSEMAT) at the Department of Chemistry, Aarhus University, invites applications for a 24-month postdoctoral position in computational materials chemistry.

The successful candidate will be employed by Aarhus University and primarily stationed in the Research Unit of Theoretical Chemistry at the Institute of Materials Chemistry, TU Wien, in the group of Prof. Georg Madsen, with regular shorter research stays at Aarhus University.

The project combines density functional theory (DFT), machine-learned force fields and atomistic simulations to uncover how correlated structural disorder controls transport and other functional properties in energy materials.

The position is available from the 1st of February 2027 or as soon as possible thereafter.

Project Description

CENSEMAT investigates crystalline energy materials in which non-random local deviations from the average structure play a decisive role. The postdoc will develop atomistic models that connect local chemical environments and correlated disorder to thermodynamic stability and transport. The research will form a central part of CENSEMAT's simulation-guided materials programme and will be carried out in close collaboration with experimental researchers working on battery electrodes, thermoelectric materials and ferroelectric relaxors.

Main Tasks

• Generate and curate ab initio reference datasets for compositionally complex, defect-containing and structurally disordered crystalline materials.
• Develop, train, validate and benchmark machine-learned interatomic force fields for multicomponent inorganic energy materials.
• Use molecular dynamics, lattice-dynamical methods and statistical sampling to investigate local disorder, phase stability, temperature-dependent behaviour and structure-property relations.
• Investigate how defects and correlated disorder affect ion-migration, lattice vibrations and thermal transport, including calculations of relevant migration pathways and energy barriers.
• Relate simulations to experimentally derived disorder models and data from methods such as total scattering/pair distribution function analysis, diffuse scattering, diffraction, solid-state NMR and electron microscopy.
• Work closely with Professor Georg Madsen and colleagues at TU Wien and with the CENSEMAT team at Aarhus University, including participation in regular shorter research stays in Aarhus.
• Communicate results through peer-reviewed publications, conference contributions and internal CENSEMAT activities, and contribute to the development of shared and reproducible computational workflows.

Your Profile

Applicants must hold a PhD, or have submitted a PhD thesis before the starting date, in computational materials science, physics, theoretical or computational chemistry, or a closely related field.

The successful candidate is expected to demonstrate most of the following qualifications:

• Strong documented experience with first-principles calculations of solids using DFT, including the generation and management of ab initio reference databases for training of machine-learned models.
• Documented experience in developing, training, evaluating and validating machine-learned interatomic force fields for atomistic simulations.
• Substantial experience with molecular dynamics and lattice-dynamical or phonon calculations for complex, defect-containing or disordered solids.
• Research experience linking local structure and defects to both lattice thermal transport and ion migration in solids.
• Strong scientific programming skills and experience with Linux-based high-performance computing, automated workflows and reproducible data analysis; proficiency in Python or a comparable language is expected.
• A publication record appropriate to career stage that demonstrates the ability to conduct and communicate independent computational research.
• Fluency in written and spoken English.
• Ability to work independently, take responsibility for complex computational projects and collaborate constructively across theoretical and experimental disciplines.
• Willingness and ability to be based primarily in Vienna while maintaining active participation in CENSEMAT through regular shorter stays in Aarhus and online collaboration.

Experience with one or more of the following will be considered an advantage: active-learning strategies for force-field construction, Green-Kubo or Boltzmann-transport methods, defect thermodynamics, and simulation-experiment integration for disordered materials.