European Synchrotron Radiation Facility (ESRF)
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PhD Student in Neural-Network-Driven Reconstruction for Scanning 3D X-ray Diffraction European Synchrotron Radiation Facility (ESRF) in France
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding available
Country
France
University
European Synchrotron Radiation Facility (ESRF)
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About this position
The European Synchrotron Radiation Facility (ESRF) in Grenoble, France, is offering a PhD student position focused on computational methods for synchrotron diffraction analysis. The project develops neural-network-driven reconstruction tools for Scanning 3D X-ray Diffraction (S3DXRD), a non-destructive technique for mapping grain-resolved orientation and strain fields in crystalline materials at sub-micron resolution.
Based at the ESRF and hosted by Université Grenoble Alpes (UGA) within the Physics doctoral school, the PhD places you at the intersection of synchrotron science, materials characterisation, scientific machine learning, and open-source software development. You will work with beamline scientists on ID11 and ID03, as well as the ESRF Algorithms & Scientific Data Analysis group, to create synthetic diffraction training data from simulated microstructures and train neural networks to automate an expert-intensive reconstruction workflow.
Key research tasks include building a phantom microstructure generation pipeline, validating synthetic diffraction against real ID11 datasets, training and testing neural-network indexing models, benchmarking automated reconstruction against conventional approaches, collaborating with crystal plasticity simulation groups, and packaging a reconstruction toolkit for public release. The project aims to make diffraction microstructure imaging faster, more robust, and more accessible to industrial users and the wider research community.
The position is suitable for candidates with an MSc, Master 2, Laurea, or equivalent 300 ECTS in Physics, Materials Science, Engineering, or a related subject that allows PhD enrolment in Physics at UGA. Strong interest in X-ray diffraction and materials characterisation is essential, together with a solid background in machine learning and Python. Experience with synchrotron or laboratory characterisation methods such as EBSD or LabDCT is an advantage. English proficiency is required.
Employment is offered as a two-year contract, renewable for one additional year. ESRF highlights a competitive compensation and allowances package, including relocation support to Grenoble, and emphasizes its international, equal-opportunity environment. Applications are made through the provided online application link. The application deadline is not specified in the posting.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
How to apply
Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.
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