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PhD candidate (h/f) in microscopy and machine learning

This offer is available in the following languages:
Français - Anglais

Date Limite Candidature : lundi 11 juillet 2022

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General information

Reference : UMR7252-PIEBON-001
Workplace : LIMOGES
Date of publication : Monday, June 20, 2022
Scientific Responsible name : Pierre Bon
Type of Contract : PhD Student contract / Thesis offer
Contract Period : 36 months
Start date of the thesis : 3 October 2022
Proportion of work : Full time
Remuneration : 2 135,00 € gross monthly

Description of the thesis topic

While the use of fluorescent probes allows highly specific imaging of a precise subset of sample molecules, it remains complex to extract equivalent information with label-free imaging approaches.
We are developing a novel label-free imaging approach based on measuring the phase component of light after interaction with the living sample (Bon et al., Biophys. J., 2014). This technique highlights everything that is not water in the sample and -coupled with machine learning- unlocks selectivity of a set of intracellular elements. This approach is now advanced enough for a use in the functional characterization of living biological samples from the adherent cell to the tissue.
The goal of the proposed PhD-thesis is to allow routine acquisition on biological samples (interfacing, experiment automation and real time) while pursuing the development of machine learning algorithms to make them compatible with 3D imaging. This will allow the study of cell lineage and senescence without the need for fluorescent labeling. This approach will allow the study of the development of tissues in vitro without disturbing them and over long observation times.

Context
- Automation of phase imaging in a tissue imaging context
- Observation of thick and dense biological samples
- Coupling with other existing imaging modalities (fluorescence, Raman spectroscopy...)
- Development of new information extraction strategies based on machine learning, usable in real time analysis


- Automation of experiments
- Cell culture
- Programming (Labview, Python/matlab)
- Machine learning
- Organizational skills & autonomy
- Interdisciplinary skills

Work Context

The XLIM laboratory based at the University of Limoges is world renowned for its research in the field of optics, imaging and interferometry.
Within the biophotonics axis, we develop new optical imaging techniques for biology.
In particular, within the ERC Starting "SpeciPhic", Pierre Bon is developing new approaches of label-free imaging for the study of biological tissues. It is in this project team that the candidate will work full time.

Constraints and risks

N.A.

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