Postdoc in Computational Discovery of Disordered Crystalline Materials

Postdoc in Computational Discovery of Disordered Crystalline Materials

Aalborg University

Aalborg, Denmark

Do you want to be part of a young, dynamic research group working on designing the next generation of sustainable energy materials using computational chemistry and machine learning? And do you see yourself as a team player with strong collaborative skills? If yes, we look forward to reading your application to our Postdoc position.

At the Faculty of Engineering and Science, Department of Chemistry and Bioscience, a two-year position as Postdoc in computational discovery of disordered crystalline materials is open for appointment from 1 July 2026 or soon hereafter.

Your work tasks

A two-year postdoc position in computational discovery of disordered crystalline materials is available to design sustainable materials with tailored disorder using computational chemistry and machine learning. Disorder in crystalline materials is often responsible for improved materials performance, but it is commonly overlooked in computational materials design. In this project, we will focus on developing accelerated computational methods to predict novel, disordered materials with application, e.g., for solid electrolytes in next-generation batteries.

The position is part of the Villum Young Investigator project titled “Entropy in materials design: Accelerated discovery of disordered solid electrolytes”, which focuses on developing computational predictions of disordered crystalline materials and applying these to discover novel solid-state electrolytes for batteries. Thus, the prospective candidate should have an interest in working in a collaborative team towards a common goal.

The postdoc will focus on developing computational datasets of disordered materials based on density functional theory calculations and training machine learning models to accelerate the predictions. This work will involve collaboration with Assoc. Prof. Alex Ganose at Imperial College London with the possibility of a 1-2 month exchange stay to develop automated computational workflows for disordered crystals. The developed models will then be applied to predict disordered solid electrolytes in collaboration with other group members.

The prospective candidate will be responsible for planning, conducting, and disseminating high-quality computational research in collaboration with other team members, and is expected to take a role in providing guidance to PhD, masters, and bachelor students. The postdoc will be part of the computational materials chemistry group led by Tenure-Track Assistant Professor, Kasper Tolborg, within the Section of Disordered Materials, which offers state-of-the-art computational and experimental infrastructure and facilitates a dynamic work environment and interdisciplinary collaborations.

All interested candidates are encouraged to apply, regardless of their personal background. We value diversity and see it as a strength.

Your competencies

The applicant is expected to demonstrate the following competencies and qualifications:

• Hold a PhD degree in chemistry, materials science, physics, nanoscience or a closely related subject;
• Have a strong publication record in computational materials science;
• Experience in density functional theory calculations of materials, and machine learning applied to chemistry or materials science;
• Experience with scientific programming (e.g. in python);
• Experience with modelling of disordered materials is seen as an advantage;
• Experience with automated computational workflows is seen as an advantage;
• Demonstrate complex problem-solving skills and critical thinking;
• Have an interest in and ability to work interdisciplinary;
• Show an integrative and cooperative personality with good communication and interpersonal skills;
• Have excellent oral and written English language skills.

Qualification requirements

Appointment as postdoc requires academic qualifications at PhD level.