Postdoctoral AI Scientist: Predicting XRD from RHEED Video

ecolecentraledelyon; Écully; Hybride; EUR 42 000 - 60 000; Plein temps

Résumé du poste

École Centrale de Lyon invites applications for a Postdoctoral Researcher to develop deep learning models that predict the final XRD spectrum of oxide thin films from RHEED growth data. The role sits at the AI–materials interface, with data from INL and LIRIS, aiming to deliver a real-time, calibrated spectrum predictor during MBE growth.

You will contribute to corpus structuring, probabilistic regression with uncertainty estimates, and a real-time demonstrator.

Qualifications

• PhD in AI/CS/ML or related field with strong publication record.
• Hands-on experience with video data and deep learning architectures.
• Proficiency in Python and ML frameworks (PyTorch or TensorFlow) are mandatory.
• Ability to work autonomously in a multidisciplinary team.
• Excellent scientific-writing and communication in English.

Responsabilités

• Develop and evaluate deep learning models to map RHEED video to final XRD spectra.
• Structure and curate versioned datasets combining video, growth parameters and ex situ spectra.
• Quantify predictive uncertainty and build probabilistic regression frameworks.
• Contribute to real-time demonstrator prototype during growth experiments.

Connaissances

Deep learning on video data

Python

PyTorch

TensorFlow

Bayesian / uncertainty quantification

Git and reproducible research

English communication

3D CNNs / temporal transformers

Probabilistic models

Formation

Ph.D. in AI/CS/ML or related field

Outils

Python

PyTorch

TensorFlow

Git

3D CNNs

Description du poste

École Centrale de Lyon invites applications for a Postdoctoral Researcher to develop deep learning models that predict the final XRD spectrum of oxide thin films from RHEED growth data. The role sits at the AI–materials interface, with data from INL and LIRIS, aiming to deliver a real-time, calibrated spectrum predictor during MBE growth.

You will contribute to corpus structuring, probabilistic regression with uncertainty estimates, and a real-time demonstrator.