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Recent advancements in quantum-mechanical (QM) methods, notably density functional theory (DFT), have significantly enhanced our understanding of materials properties, enabling the prediction of these properties a priori. High-throughput QM calculations, in particular, have established a robust framework for the exploration of new materials. Additionally, the recent progress in Machine Learning (ML) methods has facilitated the exploration of the vast phase space of materials, characterized by structural and compositional degrees of freedom, leading to breakthroughs in materials discovery.
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The research focus in our group lies at the intersection of computational materials science and ML. I am particularly interested in leveraging these approaches for the development of advanced energy materials relevant to clean energy technologies, such as hydrogen storage and catalysis.