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Reference : UMR5506-EMMFAU-003
Workplace : MONTPELLIER,MONTPELLIER
Date of publication : Thursday, October 08, 2020
Type of Contract : FTC Scientist
Contract Period : 18 months
Expected date of employment : 9 November 2020
Proportion of work : Full time
Remuneration : Between 2 437€ and 2 979€ gross monthly according to experience
Desired level of education : PhD
Experience required : Indifferent
Machine Learning and more specifically Deep Learning gives very promising results in various domains of biological image analysis. Most methods are, however, developed for 2D image datasets. The aim of the project is to develop new Deep learning methods in the field of biological imaging, and more specifically in 4D (3D plus time) cell segmentation, the recognition and tracking of each cell and its progeny in live developing embryos. The project will start from a preexisting annotated database of more than 10 fully-segmented animal embryos (>500000 annotated segmented cells). It aims at the creation of an end-to-end deep learning method for complex 3D object instance segmentation and tracking
The main activity focuses on the development of new statistical learning methods based on artificial neural networks for 4D cell segmentation and monitoring.
- development of new end-to-end methods to perform 3D segmentation instances.
- development of a new memory network method allowing to perform cellular tracking.
- bibliographical research
- participation in the coding of a machine learning library
- participation in team life
- presentation of research work
- writing publications
We are seeking a highly motivated early career scientist with a Ph.D. in machine learning and some interest in biology. Experience in cell and developmental biology is NOT required; strong coding abilities are necessary. A background in applied mathematics/computational biology and or image analysis is desirable. The candidate must have effective oral and written communication skills. There is no nationality requirement nor need to speak French.
This project is led by a strong and efficient collaboration between a computer scientist Emmanuel Faure (LIRMM - CNRS) and a biologist Patrick Lemaire (CRBM - CNRS)
Emmanuel Faure is a Data Scientist using recent machine learning techniques to understand quantitative biology and medicine. Patrick Lemaire's team focuses on animal embryonic development, using ascidians as a model system.
The position will be based in Montpellier, in the Laboratory of Computer Science, Robotics and Microelectronics
The Lab LIRMM : https://www.lirmm.fr
The Team ICAR : https://www.lirmm.fr/icar/
Emmanuel Faure : https://www.lirmm.fr/~efaure
Patrick Lemaire : http://www.crbm.cnrs.fr/en/team/lemaire/
This position is open now and is funded for one to two years. Team spirit, autonomy, dynamism, creativity would be appreciated.
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