Get A Job.ai – Postdoctoral Researcher Position Deep Learning for Functional-Oxide Growth Video-to-Spectrum Prediction by RHEED / XRD Fusion

Project Overview

We are representing a leading French research institution seeking a highly motivated Postdoctoral Researcher to develop innovative deep-learning models that predict the structure of functional-oxide thin films directly from their growth dynamics. This position sits at the cutting edge of AI and materials physics, focusing on designing a deep model capable of predicting X-ray Diffraction (XRD) spectra from Reflection High-Energy Electron Diffraction (RHEED) video data recorded during molecular beam epitaxy growth.

This is a genuinely multidisciplinary collaboration between two renowned research groups: one providing operando experimental data and materials-physics expertise, the other contributing deep video-learning and probabilistic-modeling methodology. You will work on a unique, unpublished dataset of paired RHEED videos and XRD spectra to build one of the first video-to-spectrum models with calibrated uncertainty for oxide growth—a genuine first-mover advantage in this fast-growing field.

The resulting tool will enable predictive, in-situ control of oxide epitaxy, dramatically reducing the costly trial-and-error approach currently dominating the field and accelerating materials discovery while cutting experimental time, cost, and resource consumption.

Responsibilities

You will drive the core computational research along a risk-managed, phased 12-month roadmap organized around three technical pillars:

• Corpus structuring and spatio-temporal representation: Build a versioned dataset by cleaning, temporally aligning, and pairing RHEED video sequences with MBE parameters and ex-situ XRD/XRR spectra; pre-train a spatio-temporal video encoder using self-supervised methods; perform fuzzy/Bayesian classification of growth regimes
• Probabilistic regression with uncertainty quantification: Design latent representations coupling video encoding with physical parameters; learn scalar structural parameters progressing to full 1D XRD spectrum prediction; provide calibrated uncertainty estimates using Bayesian approaches or deep ensembles
• Real-time demonstrator and dissemination: Validate models on new growths; prototype an in-growth inference tool; implement temporal data augmentation, transfer learning, and domain adaptation; contribute to high-impact joint publications at the AI/materials interface
• Work autonomously on complex, multidisciplinary challenges while collaborating across physics and computer science teams
• Maintain reproducible research practices with version control and comprehensive documentation

What We're Looking For

Required qualifications:

• Ph.D. in Artificial Intelligence, Computer Science, Machine Learning, Computer Vision, or Signal Processing
• 2–5 years of post-PhD experience as a senior postdoctoral researcher
• Demonstrable hands-on experience with deep learning on sequential/video data, including 3D CNNs, temporal transformers, and/or probabilistic models
• Proficiency in Python and PyTorch or TensorFlow (mandatory)
• Strong curiosity for or prior exposure to experimental physics and materials science—ideally an AI-focused Ph.D. graduate eager to apply expertise to real materials-physics problems
• Track record of innovation and ability to work autonomously on complex projects
• Experience with Git version control and reproducible research workflows
• Excellent communication and scientific-writing skills in English

Highly valued additional skills:

• 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

How We Work With You

Our talent team at Get A Job.ai partners with leading research institutions across Europe to identify exceptional candidates for specialized positions. When you apply through our platform, a dedicated recruiter will screen your application and discuss the opportunity in detail. We then coordinate your submission to our client and support you throughout the interview process. Please do not contact the institution directly—all applications and communications should go through Get A Job.ai to ensure proper consideration.

To apply, please prepare a single PDF containing: (1) a detailed CV including publication list, (2) a one-page motivation letter explaining your specific interest in this project and how your background addresses the key responsibilities, and (3) contact information for at least two academic references.

Location and Employment Details

This 12-month postdoctoral position is based in Écully, Auvergne-Rhône-Alpes, France. The role offers an exceptional opportunity to work at the intersection of AI and experimental materials science within a collaborative, world-class research environment.

Pay

Compensation details will be discussed during the screening process and are competitive with French postdoctoral research standards.

Equal Employment Opportunity: Get A Job.ai is committed to inclusive hiring practices. We welcome applications from candidates of all backgrounds and work with our client partners to ensure fair and equitable recruitment processes.