Postdoctoral Research Associate

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.