Postdoc in first-principles modelling and machine-learned force fields for correlated disorder in 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

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

Institut for Kemi Langelandsgade 140 8000 Aarhus C

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.

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.

• 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.

CENSEMAT

The Center for Sustainable Energy Materials (CENSEMAT) is a Center of Excellence funded by the Danish National Research Foundation and based at Aarhus University. Its ambition is to develop sustainable materials for energy conversion and storage by treating correlated structural disorder as a design parameter. The centre brings together expertise in materials synthesis, advanced scattering and spectroscopy, electron microscopy, solid-state NMR, electrochemistry, property measurements and computational modelling. Its research spans battery electrodes, thermoelectric materials and ferroelectric relaxors.

The computational environment

The postdoc will be scientifically embedded in Professor Georg Madsen's Research Unit of Theoretical Chemistry at the Institute of Materials Chemistry, TU Wien. The group develops and applies electronic-structure theory, machine-learning methods and predictive calculations of defects and transport properties. The position will also be integrated into the wider CENSEMAT research environment at Aarhus University, allowing computed models and predictions to be tested directly against advanced experimental characterisation.

Department of Chemistry

The Department of Chemistry at Aarhus University is a leading European chemistry department with broad research programmes and a highly international environment. CENSEMAT is hosted within the department and provides a collaborative setting for researchers working across materials chemistry, crystallography, spectroscopy, computation and energy science.

The position offers an unusual opportunity to work across two strong and complementary research environments. English is the working language in both groups. The postdoc will have access to scientific mentoring, international networks, high-performance computing resources and close interaction with experimental materials scientists. CENSEMAT and the TU Wien research group value scientific curiosity, openness, constructive teamwork and a healthy work-life balance.

All qualified candidates are encouraged to apply regardless of personal background. Aarhus University views equality and diversity as assets and aims to provide an inclusive environment in which each individual can thrive and develop.

Place of work

The successful candidate will be employed by Aarhus University and will be stationed primarily at the Research Unit of Theoretical Chemistry, Institute of Materials Chemistry, TU Wien, Getreidemarkt 9/165, 1060 Vienna, Austria. Regular shorter research stays at the Department of Chemistry, Aarhus University, Langelandsgade 140, 8000 Aarhus C, Denmark, are an integral part of the position.

Contact information

For further information about the position, please contact: Professor Dorthe Bomholdt Ravnsbæk, Department of Chemistry, Aarhus University: [e-mail], [phone]