PhD in Materials ML for X-ray Diffraction Imaging

ESRF - The European Synchrotron Radiation Facility; Grenoble; Hybrid; EUR 26,000 - 38,000; Full time

Job summary

ESRF - The European Synchrotron Radiation Facility in Grenoble invites applications for a PhD student position. You will develop neural-network-driven reconstruction tools for Scanning 3D X-ray Diffraction (S3DXRD) to map grain-resolved orientation and strain in crystalline materials with sub-micron resolution.

Working with beamline scientists on ID11, ID03 and the Algorithms & scientific Data Analysis group, you will generate synthetic diffraction data from simulated microstructures and train

Qualifications

• Degree in Physics, Materials Science, Engineering or related field qualifying for PhD enrollment in Physics at UGA.
• Strong interest in X-ray diffraction and materials characterisation techniques.
• Solid background in machine learning techniques.
• Experience with Python.
• Prior synchrotron or lab-based characterisation techniques (EBSD, LabDCT) a plus.

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 a range of materials.
• Collaborate with crystal plasticity simulation groups to ensure training microstructures capture orientation gradient fields.
• Package, document and release an open-source reconstruction toolkit for the community.

Skills

X-ray diffraction

Materials characterization

Machine learning

Python

English proficiency

Education

MSc / Master 2 / Laurea or equivalent 300 ECTS in Physics, Materials Science, Engineering

Job description

ESRF - The European Synchrotron Radiation Facility in Grenoble invites applications for a PhD student position. You will develop neural-network-driven reconstruction tools for Scanning 3D X-ray Diffraction (S3DXRD) to map grain-resolved orientation and strain in crystalline materials with sub-micron resolution.

Working with beamline scientists on ID11, ID03 and the Algorithms & scientific Data Analysis group, you will generate synthetic diffraction data from simulated microstructures and train