CENTRALE LYON - Postdoctoral Researcher Position Deep Learning for Functional-Oxide Growth Video-to-Spectrum Prediction by RHEED / XRD Fusion

CENTRALE LYON·Ecully, Auvergne-Rhône-Alpes, France

En corto

→Desarrollar modelos de deep learning para predecir espectros XRD a partir de videos RHEED durante el crecimiento de óxidos funcionales.
→Trabajar con datos experimentales en tiempo real y espectros ex situ para crear un sistema predictivo con incertidumbre calibrada.
→Proyecto innovador y pionero en IA para física de materiales, con impacto en acelerar el desarrollo de materiales de alta eficiencia.

Fluency in English is required for collaboration and publication.

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¿Qué piden?

✓Título de doctorado en Ciencias de la Computación, Física, Ingeniería o campo afín.
✓Experiencia comprobada en modelado profundo (deep learning) con videos o secuencias temporales.
✓Habilidad en modelado probabilístico o inferencia bayesiana.
✓Conocimiento en procesos de crecimiento de materiales (MBE) o ciencia de materiales funcionales (ventaja).
✓Capacidad para trabajar en colaboración multidisciplinar (física experimental + AI).
✓Publicaciones relevantes en AI, visión por computadora o ciencia de materiales.

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Deep Learning3D CNNR(2+1)DTemporal Vision TransformerSelf-supervised LearningProbabilistic RegressionBayesian ClassificationSpatio-Temporal EncodingRHEEDXRD/XRR

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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 conditions. MBE offers independent control of each element but, applied to oxides, is notoriously unstable and poorly reproducible because of the oxidising atmosphere. RHEED tracks the surface dynamics in real time (lattice parameter, surface reconstructions, roughness) but does not give access to the final bulk microstructure, which is only reachable ex situ via XRD / XRR. There is therefore a strong physical link – so far unexploited quantitatively – between RHEED dynamics and final XRD microstructure, which OXYD-IA proposes to learn directly. Figure 1 – OXYD-IA pipeline. At inference, the RHEED video acquired during growth (INL) feeds a deep video encoder (LIRIS) and a probabilistic regression module that predicts the final XRD spectrum together with its uncertainty. During training, the predicted spectrum is compared with the ex situ XRD / XRR spectrum measured at INL (ground truth); this discrepancy is the supervised learning signal that adjusts the model parameters. KEY RESEARCH AREAS AND RESPONSIBILITIES You will drive the core computational research along a risk-managed, phased 12-month roadmap. The primary target is a versioned RHEED / XRD dataset, growth-regime classification and probabilistic regression of the key structural parameters with calibrated uncertainty; full-spectrum prediction, a real-time demonstrator and cross-oxide generalisation are stretch and follow-on objectives. The work is organised around three technical pillars: 1. Corpus structuring and spatio-temporal RHEED representation • Build a versioned dataset by cleaning, temporally aligning and pairing the RHEED video sequences, the associated MBE parameters (temperature, oxygen pressure, source fluxes) and the ex situ XRD / XRR spectra (SrTiO3 reference system). g. R(2+1)D, or temporal Vision Transformer) fine-tuned on the laboratory corpus.

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