Internship and thesis proposals
Physics-informed machine learning for the inference of mechanical properties of living tissues

Domaines
Biophysics
Soft matter
Physics of liquids
Physics of living systems
Hydrodynamics/Turbulence/Fluid mechanics

Type of internship
Théorique, numérique
Description
During development, living tissues grow and undergo major deformations to form functional organs. These deformations are controlled by the complex and tunable mechanical properties of tissues, which thus play a fundamental role during morphogenesis. However, little is known about these mechanical properties and how they affect tissue deformation, in particular because probing these properties in real tissues remains an interdisciplinary challenge. In this project, we collaborate with experimentalists developing microfluidic approaches, in which living embryonic tissues are aspired through microchannels and imaged using live microscopy. Our goal is to use these experimental images to infer tissue mechanical properties from the observed deformations.

Contact
Simon Gsell
Laboratory : IRPHE - UMR7342
Team : milieu vivant systemes biologiques
Team Website
/ Thesis :    Funding :