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Reference : UMR7332-CATLEV-011
Workplace : MARSEILLE 09
Date of publication : Monday, February 15, 2021
Type of Contract : FTC Scientist
Contract Period : 12 months
Expected date of employment : 1 May 2021
Proportion of work : Full time
Remuneration : 4550 et 5260 € bruts mensuels selon expérience.
Desired level of education : PhD
Experience required : 1 to 4 years
- Develop a set of theoretical tools to predict the mechanical and hydrodynamic properties of granular media and/or cellular tissues from image sequences of their spontaneous dynamics ;
- Generate simulation data to train algorithms for the recognition of pre-cancerous tissues;
- analyze experimental data from biopsies.
- Conceptualize new paradigms of active matter inspired by experimental data.
- Create new numerical simulations tools either cell-based (vertex model) or continuum (hydrodynamic transport equations)
- Contribute to the piloting and technical training of students.
- Computer science : Programming in Matlab, C++, Python.
- Theory : Hydrodynamics of complex systems, Statistical Physics, Inference Methods, Artificial Intelligence.
- English language : B2 to C1.
- Managing a project
- Autonomy to develop new areas of research
- Ensure a bibliography watch
- Write scientific articles.
Scientific context The majority of cancers originate from epithelial tissues that are formed of cells lining the surface of organs. These living tissues reorganize, flow and renew themselves on time scales of a few days to a few months. We seek to develop of an image-based algorithm to infer the local rigidity of epithelial tissues, with potential application for early pre-cancerous diagnosis.
Constraints and risks
Working environment Our theoretical research group develops analytical and numerical models to understand the physics of living materials. We have implemented a numerical simulation tool for epithelial tissues called the vertex model whose dynamics result from a mechanical equilibrium between a set of cellular forces, including viscosity, interfacial tension and cellular pressure. The originality of our approach is to consider the effect of fluctuations in mechanical activity; we consider extensions of theorems from physics and statistics to link response function and correlation function in living materials, with the aim of defining tools for tissue micro-rheology. We propose to use our vertex model to simulate tissues whose mechanical properties are controlled and whose spectrum of fluctuations is imposed.
Depending on the interest and skills of recruited candidate, we could also consider the development of supervised deep learning techniques to infer the mechanical properties of these simulated tissues.
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