About us
Espace utilisateur
INSTN offers more than 40 diplomas from operator level to post-graduate degree level. 30% of our students are international students.
Professionnal development
Professionnal development
Find a training course
INSTN delivers off-the-self or tailor-made training courses to support the operational excellence of your talents.
Human capital solutions
At INSTN, we are committed to providing our partners with the best human capital solutions to develop and deliver safe & sustainable projects.
Home   /   Post Doctorat   /   Nano-imaging with deep neural networks

Nano-imaging with deep neural networks


The postdoctoral research project is part of a five-year ERC-funded project called CARINE (Coherent diffrAction foR a Look Inside NanostructurEs towards atomic resolution: catalysis and interfaces – https://carine-erc.eu) to develop and apply new coherent diffraction imaging (CDI) capabilities. We want to develop and apply machine learning and, more generally, data science approaches for imaging and characterisation of nanoscale systems. Coherent x-ray diffraction imaging is a strong new tool to probe the structure of nanomaterials in a non-destructive way with a spatial resolution of 10 nm. The reconstruction problem, known as “phase retrieval”, is typically solved by iterative algorithms that do not always converge. Machine learning will be applied to different tasks like e.g. phase retrieval, super-resolution, phase unwrapping, etc, to unambiguously reverse the diffraction patterns and image the structure of 3D object with nm-resolution.


Institut de Recherche Interdisciplinaire de Grenoble
Top envelopegraduation-hatlicensebookuserusersmap-markercalendar-fullbubblecrossmenuarrow-down