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

United States Digital Space LLC; Écully; Sur place; EUR 42 000 - 54 000; Plein temps

Résumé du poste

United States Digital Space LLC is seeking a Postdoctoral Researcher to develop deep learning models predicting XRD spectra from RHEED video data in a materials-physics context. You will collaborate with INL, LIRIS, and École Centrale de Lyon to create a first-of-its-kind video-to-spectrum model with uncertainty estimates.

The role emphasizes autonomous work, rigorous validation on experimental data, and the dissemination of findings through joint publications in AI and materials science.

Qualifications

• PhD in AI/CS/ML required or equivalent
• Hands-on experience with deep learning on sequential/video data
• Proficiency in Python and PyTorch is mandatory
• Excellent English communication and scientific-writing skills

Responsabilités

• Lead development of deep video models from growth datasets (RHEED/XRD)
• Implement Bayesian or ensemble uncertainty quantification for predictions
• Collaborate with interdisciplinary teams to publish results and disseminate methodology

Connaissances

Deep learning

Python

PyTorch

Version control

Scientific writing

English

Physics interest

Formation

PhD in AI/CS/ML

Outils

TensorFlow

Description du poste

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

• 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). • Pre-train, in a self-supervised manner, a spatio-temporal video encoder (3D CNN, e.g. R(2+1)D, or temporal Vision Transformer) fine-tuned on the laboratory corpus. • Perform fuzzy / Bayesian classification of the growth regimes (2D vs. 3D modes, transitions, RHEED oscillations), building on LIRIS expertise in the statistical analysis of sequences.
• 2. Probabilistic RHEED →XRD regression with uncertainty quantification • Design a latent representation that couples the video encoding with the physical MBE parameters. • Learn stepwise – first scalar structural parameters (lattice parameter, density, mosaicity), more robust at small N, then the full 1D XRD spectrum. • Provide calibrated uncertainty estimates (Bayesian approaches or deep ensembles), indispensable for guiding real-time decisions during growth.
• 3. Real-time demonstrator and dissemination (stretch / follow-on) • Validate the model on new SrTiO3 growths and prototype an in-growth inference tool at the end of the NANOFUTUR MBE line. • Mitigate the small-data and generalisation challenges through temporal data augmentation, transfer learning from pre-trained video encoders and domain adaptation towards other functional oxides. • Contribute toward a high-impact joint publication at the AI / materials interface (the project milestone is the submission of a joint paper).

REQUIRED QUALIFICATIONS

• A Ph.D. in the field of Artificial Intelligence – Computer Science, Machine Learning, Computer Vision or Signal Processing, and we are looking for a senior postdoc with 2–5 years of post-PhD experience.
• Deep learning on sequential / video data: demonstrable, hands‑on experience with 3D CNNs, temporal transformers and / or probabilistic models. Proficiency in Python and PyTorch (or TensorFlow) is mandatory.
• A genuine appetite for physics: a strong curiosity for, or prior exposure to, experimental physics and materials science. The ideal candidate carried out an AI‑focused Ph.D. and is eager to apply it to a real materials‑physics problem.
• Research mindset: a track record of innovation and the ability to work autonomously on a complex, multidisciplinary project. • Software skills: experience with Git version control and reproducible research.
• Excellent communication and scientific‑writing skills in English.

ADDITIONAL SKILLS AND COMPETENCIES

• Self-supervised video pre‑training, uncertainty quantification (Bayesian / ensembles), fuzzy classification, or domain adaptation.
• Experience with small‑sample / transfer‑learning settings and handling large‑scale experimental datasets.