We are looking for brilliant computational-materials-science researchers with a strong background in artificial intelligence for a collaborative research project between the Leibniz Institute for Crystal Growth (IKZ Berlin) and the NOMAD Laboratory at the Fritz Haber Institute and the Humboldt University in Berlin. The ambitious aim of the project is to identify novel memristive materials, by combining synthesis, theory, and artificial-intelligence-driven workflows.

The research moves from a recent breakthrough at IKZ on a novel possible class of memristors [1], and the AI advances in compressed-sensing-based symbolic regression and subgroup discovery from the NOMAD group [2-4].

Both a 3-year postdoc and a PhD position are available. Together with an outstanding materials-science research curriculum, strong scientific-programming skills and experience in data mining are required.

The interested candidates should apply to Dr. Luca Ghiringhelli ([email protected]) providing a motivation letter, scientific CV, including 3 contact persons for a reference letter, and an essay of the programming skills (e.g., a project on github)

[1] Baki, A. et al. Influence of Sr deficiency on structural and electrical properties of SrTiO 3 thin films grown by metal-organic vapor phase epitaxy. Sci. Reports 11, 7497.

[2] Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M. & Ghiringhelli, L. M. SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2, 83802 (2018).

[3] Ouyang, R., Ahmetcik, E., Carbogno, C., Scheffler, M. & Ghiringhelli, L. M. Simultaneous learning of several materials properties from incomplete databases with multi-task SISSO. JPhys Mater. 2, 24002 (2019).

[4] Sutton, C. et al. Identifying domains of applicability of machine learning models for materials science. Nat. Commun. 11, (2020).