Postdoc: Video-to-Spectrum AI for Oxide Growth (RHEED→XRD)

Centrale Lyon; Écully; On-site; EUR 42,000 - 54,000; Full time

Job summary

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

Responsibilities

• 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

Skills

Deep learning

Python

PyTorch

Version control

Scientific writing

English

Physics interest

Education

PhD in AI/CS/ML

Tools

TensorFlow

Job description

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.