PhD thesis
There is no PhD offer at the moment.
MSc Internships
Low-rank tensor models for super-resolution fluorescence imaging
The property of biological tissues to naturally emit photons is called autofluorescence. When studying wheat, leveraging the information obtained from autofluorescence allows one to study the grain’s grows. This is crucial in a context of climate change and global population increase. The analysis of the chemical compounds (proteins) in the wheat allows one to measure its nutritional values as well as its resistance at diverse stages of its growth.
In June 2024, experiments were conducted at the Synchrotron SOLEIL particle accelerator within a partnership with LASIRe (Univ. Lille), ENS Paris-Saclay and INRAE (Nantes). These experiments allowed for the acquisition of wheat grain images with high resolution using microscopes, and were permitted by the intense and highly tunable light source available in the Synchrotron. The resulting observations are data cubes with two spatial dimensions (the pixels) and a spectral dimension that contains the autofluorescence spectra. The acquisitions were conducted with the two microscopes of the DISCO UV beamline. The full-scope microscope Telemos covers the whole spatial region comprised in the sample with a high spatial resolution (500nm) but with a low spectral resolution (approx. 10 spectral bands) within the spectral range from 280nm to 560nm. Conversely, the microspectrofluorimeter Polypheme allows one for the acquisition of a small portion of a sample with a low spatial resolution (1μm to 5μm) but with a high spectral resolution spanning the range 190-900nm with hundreds of spectral bands.
In order to fully leverage the complementary resolutions of each acquisition device, and to optimize the beam time obtained by Synchrotron users, one may seek for images with high-resolution in each one of the three dimensions. This internship hence aims at designing super-resolution methods for fluorescence Synchrotron images. The proposed algorithms must be compatible with the large volume spanned by the data as well as the high variability in the acquisition process.
Please find the detailed proposal here
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