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European Synchrotron Radiation Facility

PhD Student in Neural-Network-Driven Reconstruction for Scanning 3D X-ray Diffraction European Synchrotron Radiation Facility in France

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Sep 4, 2026

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Country

France

University

European Synchrotron Radiation Facility

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Keywords

Computer Science
Machine Learning
Mathematics
Python Programming
X-ray Diffraction
Material Characterization
Artificial Neural Network
Open-source Software
Synchrotron Science
Crystalline Materials
Physics

About this position

PhD opportunity at the European Synchrotron Radiation Facility (ESRF) in Grenoble, France, focused on developing neural-network-driven reconstruction tools for Scanning 3D X-ray Diffraction (S3DXRD). The project sits at the intersection of synchrotron science, materials characterisation, scientific machine learning, and open-source software development.

You will work with beamline scientists on ID11, ID03, and the ESRF Algorithms & scientific Data Analysis group to generate synthetic diffraction training data from simulated microstructures, train neural networks for indexing and reconstruction, and validate automated methods against conventional workflows. The scientific goal is to make grain-resolved orientation and strain mapping in crystalline materials faster, more robust, and more accessible to industrial users.

Key responsibilities include building a phantom microstructure generation pipeline, validating synthetic diffraction against real datasets, training and benchmarking neural-network indexing models from box-beam to full scanning geometry, collaborating with crystal plasticity simulation groups, and releasing an open-source reconstruction toolkit for the diffraction microstructure imaging community.

The PhD is hosted by Université Grenoble Alpes (UGA) within the Physics doctoral school and is based at the ESRF in Grenoble. The post is suitable for candidates with a background in Physics, Materials Science, Engineering, or a related field, and should be attractive to applicants interested in X-ray diffraction, advanced materials characterisation, Python programming, and machine learning.

Funding is provided through a paid PhD contract of two years, renewable for one additional year, with competitive compensation, allowances, and relocation support to Grenoble. The application deadline is 4 September 2026 at 23:59 (Europe/Paris).

For further information, contact James Ball at [email protected].

Funding details

Available

What's required

Applicants should hold an MSc, Master 2, Laurea, or equivalent 300 ECTS degree in Physics, Materials Science, Engineering, or a related field that qualifies for PhD enrollment in Physics at Université Grenoble Alpes. The post requires a strong interest in X-ray diffraction and materials characterisation techniques, a solid background in machine learning techniques, experience with Python, and proficiency in English. Prior synchrotron or laboratory-based characterisation experience such as EBSD or LabDCT is a plus. The ideal candidate will be motivated, independent, and collaborative.

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