PROJECT OVERVIEW We are looking for a highly motivated Postdoctoral Researcher to develop innovative deeplearning models that predict the structure of functional-oxide thin films directly from their growth dynamics.
Positioned at the interface between Artificial Intelligence and materials physics, the OXYD-IA project aims to design a deep model able to predict the final X-ray Diffraction (XRD) spectrum of an oxide thin film from the sole Reflection High-Energy Electron Diffraction (RHEED) video recorded during its growth by Molecular Beam Epitaxy (MBE).
The resulting tool will open the way to predictive, in situ control of oxide epitaxy – a process today dominated by a costly trial-and-error approach, in which the structural and functional properties of the films are only validated ex situ.
You will join a genuinely multidisciplinary collaboration between the INL (Institut des Nanotechnologies de Lyon), which provides the operando experimental data and materials-physics expertise, and the LIRIS (équipe Imagine), which provides the deep video-learning and probabilistic-modelling methodology – both at École Centrale de Lyon.
A high-impact opportunity.
AI for experimental physics is a fast-growing field, and OXYD-IA offers a genuine first-mover advantage within it: you would work on a unique, unpublished dataset of paired RHEED videos and XRD spectra to build one of the first video-to-spectrum models with calibrated uncertainty for oxide growth.
By learning directly from experimental 1 data, such a model can short-circuit the traditional trial-and-error loop, accelerate discovery and drastically cut experimental time, cost, precursor consumption and instrument occupancy – turning routine in situ diagnostics into predictive tools.
Scientific context.
Epitaxial perovskite-oxide thin films exhibit rich, tunable functional properties (thermoelectricity, ferroelectricity, piezoelectricity) governed by their structure and composition, themselves correlated with the growth.