CFR502-PhD Student

Context & Job description

As a PhD student based at the ESRF in Grenoble, you will develop neural-network-driven reconstruction tools for Scanning 3D X-ray Diffraction (S3DXRD), a technique capable of mapping grain-resolved orientation and strain fields in crystalline materials non-destructively at sub-micron resolution. Working with beamline scientists on ID11, ID03, and the ESRF Algorithms & scientific Data Analysis group, you will generate synthetic diffraction training data from simulated microstructures and use these to train neural networks that replace the current expert-intensive reconstruction process with something fast, robust, and accessible to industrial users. The project spans methods development, experimental validation, and open-source software release, sitting at the intersection of synchrotron science, materials characterisation, and scientific machine learning. The PhD is hosted by Université Grenoble Alpes (UGA) within the Physics doctoral school.

Responsibilities include:

·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 including deformed and additively manufactured samples

·Collaborate with crystal plasticity simulation groups to ensure training microstructures capture realistic orientation gradient fields

·Package, document and release an open-source reconstruction toolkit for the broader diffraction microstructure imaging community

Further information may be obtained from James Ball (tel.: Afficher le numéro de téléphone, email: Voir email sur esrf.gestmax.eu).

Expected profile

·Degree (MSc, Master 2, Laurea, or equivalent 300 ECTS) in Physics, Materials Science, Engineering, or a related field that qualifies for PhD enrollment in Physics at UGA

·Strong interest in X-ray diffraction and materials characterisation techniques

·A solid background in machine learning techniques

·Experience with Python

·Prior synchrotron or lab-based charaterisation techniques (EBSD, LabDCT) is a plus

·Motivated, independent, and collaborative mindset

·Proficiency in English (working language at the ESRF)

Working conditions

Contract of two years renewable for one year.

Do you recognize yourself in this description? Apply now for your next professional adventure!

What we offer:

1. Join an innovative international research institute, with a workforce from 38 different countries
2. Collaborate with global experts to advance science and address societal challenges
3. Come and live in a vibrant city, in the heart of the Alps, and Europe's Green Capital 2022
4. Enjoy a workplace designed to support your quality of life
5. Benefit from ourcompetitive compensation and allowances package, including financial support for your relocation to Grenoble

For further information on employment terms and conditions, please refer to https://www.esrf.fr/home/Jobs/what-we-offer.html

The ESRF is an equal opportunity employer and encourages applications from disabled persons.