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As part of the ANR OVOPAUSE project (ANR-22-CE45-0017), the objective is to develop a model for the
automatic detection and classification of ovarian follicles, based on 2D histological sections in mice and
3D imaging from clearing tissue in fish. Ovarian follicles are multi-cellular structures that contain female
germ cells. The maturation of the follicles goes through successive growth stages until, for some of
them, ovulation or laying and the release of the mature oocyte. The distribution of follicles in the
different stages of maturity, during life, determines the reproductive state of individuals and certain
fertility disorders are associated with a disturbed distribution. The counting of ovarian follicles, and their
classification, is therefore a major challenge both for research in reproductive biology and in clinical
applications.
Manual counting of ovarian follicles remains an extremely tedious task and has led to the recent
development of artificial intelligence approaches. However, to date, no method is fully satisfactory and a
global improvement in automatic follicle classification and counting is awaited in this field.
This post-doctoral position will be carried out in a highly interdisciplinary environment, close to experts
in reproductive biology and modeling. A sufficient body of data has already been acquired in two model
species, the mouse and the medaka.
The contract can start now and, at the latest, before November 1st 2024. See attached document and link below to apply.

https://recrutement.inria.fr/public/classic/en/offres/2024-07108