PhD Candidate - ML-Powered X-ray Diffraction Imaging

ESRF - The European Synchrotron

Grenoble

On-site

EUR 20,000 - 27,000

Full time

14 days+

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Benefits offered by this job

Relocation support

International research institute

Competitive compensation package

Job summary

The ESRF in Grenoble invites applications for a PhD student position focusing on neural-network driven reconstruction for Scanning 3D X-ray Diffraction (S3DXRD). You will generate synthetic diffraction data and train neural networks to accelerate reconstruction, collaborating with ID11, ID03, and the ESRF Algorithms & scientific Data Analysis group.

Hosted by Université Grenoble Alpes, this two-year renewable PhD blends physics, materials science, and machine learning with open-source software

Qualifications

Degree (MSc, Master 2, Laurea) or equivalent qualifying for PhD enrollment in Physics at UGA.
Strong interest in X-ray diffraction and materials characterization techniques.
Solid background in machine learning techniques.
Proficiency in Python.

Responsibilities

Develop a phantom microstructure generation pipeline and validate synthetic diffraction output against real ID11 datasets.
Train and validate neural network indexing models, progressing from box-beam to full scanning geometry.
Benchmark the automated pipeline against conventional reconstruction across materials including deformed and additively manufactured samples.
Collaborate with crystal plasticity simulation groups to capture realistic orientation gradient fields in training data.
Package, document and release an open-source reconstruction toolkit for the diffraction microstructure imaging community.