Postdoc Position in Computational Materials Chemistry at the Mads Clausen Institute

We are seeking an enthusiastic new colleague as a Postdoctoral Research Associate in Computational Materials Chemistry, with a strong background in first-principles (DFT) calculations, computational materials science, and machine-learning-driven materials discovery. The successful candidate is expected to have experience in areas such as high-throughput DFT calculations, thermodynamic and phase stability analysis, crystal structure prediction, materials informatics, and machine learning, preferably applied to inorganic functional materials. Knowledge of solid-state chemistry, perovskite-related materials, and materials databases would be highly advantageous. You are invited to join the Mads Clausen Institute (MCI) at the University of Southern Denmark (SDU) in Sønderborg, Denmark. The position is for two years, with an expected starting date of 01.03.2027.

MCI is a part of the University of Southern Denmark (SDU) and covers research and education, including applications of engineering sciences. Our working language is English, reflecting the fact that far more than half of our students and staff have international roots. We are located at SDU’s beautiful campus in Sønderborg, in the inspiring building of Alsion, which is a centre for science, culture and learning in the heart of Sønderborg and perched on the strait of water separating the island of Als from the mainland. The university has very close relations with industries in Sønderborg, Southern Denmark, and Northern Germany.

The successful candidate is expected to carry out research mainly within a project funded by the VILLUM Foundation. The project is hosted by the nanotechnology centre NanoSYD, part of the Mads Clausen Institute (MCI), and integrated into CatMatX, a research laboratory led by Professor Mirabbos Hojamberdiev and focused on developing next-generation functional materials for sustainable energy and environmental applications. The research will focus on the machine-learning-driven computational discovery of novel double-perovskite oxynitrides, integrating first-principles (DFT) calculations, high-throughput computational screening, thermodynamic and phase-stability analysis, materials informatics, and machine-learning approaches. Particular emphasis will be placed on predicting the formability, thermodynamic stability, crystal and electronic structures, cation/anion ordering, and functional properties of previously unexplored double-perovskite oxynitrides. The computational predictions will be closely integrated with experimental synthesis, structural characterization, and evaluation of selected materials for solar water splitting within CatMatX. The successful candidate is also expected to contribute actively to the supervision of MSc and PhD students affiliated with SDU NanoSYD, preparation of project reports, presentations, and scientific publications, and development of research proposals supporting ongoing and future activities at SDU NanoSYD.

Job Description and Responsibilities

• Implementation of research tasks related to the project and CatMatX Lab;

• Collaborative and independent research planning focused on the machine-learning-driven computational discovery of double-perovskite oxynitrides;

• Development and application of first-principles (DFT) calculations and high-throughput computational workflows for predicting crystal structures, formability, thermodynamic stability, electronic properties, and cation/anion ordering;

• Development and application of materials-informatics and machine-learning approaches for accelerated screening and identification of promising double-perovskite oxynitrides;

• Construction, curation, and analysis of computational materials datasets, including integration of data from first-principles calculations and available materials databases;

• Independent computational data analysis, interpretation of research results, and identification of promising materials for experimental synthesis and validation in close collaboration with experimental researchers;

• Scientific communication and presentation of research results at group seminars, scientific conferences, and project meetings;

• Supervision and co-supervision of MSc and PhD students and contribution to collaborative research activities within CatMatX and NanoSYD;

• Publication of project results in international peer-reviewed scientific journals and contribution to the preparation of research proposals and project reports.

• Supervision of BSc, MSc, and exchange/visiting students conducting research on related topics;

• Assistance with teaching activities;

• Assistance with the establishment and development of research infrastructure;

• Contribution to the preparation of grant proposals for external research funding.

Potential Responsibilities 

• Supervision of BSc, MSc, and exchange/visiting students conducting research on related topics;

• Assistance with teaching activities;

• Assistance with the establishment and development of research infrastructure;

• Contribution to the preparation of grant proposals for external research funding.

 Mandatory qualifications 

• PhD degree in Computational Materials Chemistry, Computational Materials Science, Theoretical Chemistry, Condensed Matter Physics, Chemical Physics, or a closely related field;

• Strong theoretical background and demonstrated hands-on experience in first-principles calculations based on density functional theory (DFT);

• Proven experience with widely used electronic-structure codes, such as VASP, Quantum ESPRESSO, GPAW, CASTEP, or similar computational packages;

• Experience in calculating and interpreting electronic, structural, magnetic, and/or thermodynamic properties of materials;

• Experience with computational modelling of solid-state materials, preferably including transition-metal compounds, mixed-anion materials, magnetic materials, and/or catalytic materials;

• Experience with machine learning, high-throughput computational screening, materials databases, and/or data-driven materials discovery;

• Strong programming and scripting skills, preferably in Python, and experience with computational workflows and high-performance computing (HPC) environments;

• A strong track record of publications in international peer-reviewed scientific journals, including publications as a leading author;

• Excellent command of English, both written and spoken;

• Ability to conduct research independently and work effectively as part of a multidisciplinary team;

• Strong social, communication and scientific presentation skills;

• Willingness and ability to collaborate closely with experimental researchers and external research partners.

The following qualifications are advantageous

• Knowledge and/or research experience in solid-state chemistry, materials chemistry, and inorganic materials synthesis;

• Experience with mixed-anion compounds, particularly transition-metal oxynitrides and nitrides;

• Experience with materials characterization techniques, such as XRD, XPS, SEM/TEM, Raman, and UV–Vis spectroscopy;

• Research experience with advanced characterization techniques at synchrotron and/or neutron radiation facilities;

• Knowledge and/or research experience in electrochemistry, photoelectrochemistry, photocatalysis, and solar water splitting;

• Experience in connecting computational predictions with experimental synthesis, characterization, and materials performance;

• Proven experience in national and/or international collaborative research projects and strong interest in interdisciplinary research.

If you experience technical problems, please contact our email support. 

Application 

Applicants are advised to read the SDU information on how to apply. 

Assessment of the candidates is based on the application material, and the application must include the following documents in English: 

• A one-page motivation letter describing the applicant’s previous research experience and its relevance to the offered position;

• Curriculum Vitae (CV);

• Certificates and diplomas for Bachelor’s, Master’s, and PhD degrees, including transcripts in Danish and/or English;

• A complete list of publications, with publications most relevant to the offered position clearly highlighted, especially the first-authored publications;

• A research statement outlining computational strategies for the discovery and investigation of novel transition-metal-based mixed-anion compounds, particularly (oxy)nitrides, including first-principles calculations and machine-learning-driven materials discovery;

• A reference letter may be submitted but is not required.

The files should not exceed 10 MB.

The application deadline is November 30, 2026 at 11:59 PM/23:59 (CET/CEST)  

Read more and apply at https://www.sdu.dk/en/om-sdu/job-sdu