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Portail > Offres > Offre UMR8197-NATBOI-085 - Post doctorat en deep learning pour le diagnostic du Paludisme (18 mois H/F)

Post PhD deep learning for the diagnosis of malaria (18 months M/F)

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

Date Limite Candidature : lundi 20 septembre 2021

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General information

Reference : UMR8197-NATBOI-085
Workplace : PARIS 05
Date of publication : Monday, August 30, 2021
Type of Contract : FTC Scientist
Contract Period : 18 months
Expected date of employment : 1 November 2021
Proportion of work : Full time
Remuneration : Between 2700 and 3800 € monthly gross salary according to experience
Desired level of education : PhD
Experience required : Indifferent

Missions

For ten years, the deep learning approaches has enabled the community to get impressive results for supervised learning tasks on large data of any type and especially pictures. In addition, biology and medicine have recently been recognized as the research fields producing the greatest amounts of data. Thus, biological research, medicine and diagnosis are environment of choice for researchers and engineers in computer and information sciences which seek to develop sophisticated algorithms related to current scientific questions.

Robotics combined with high-throughput microscopy can generate hundreds of thousands of images from automated experiments. This type of data makes it possible to train deep network models to perform cell phenotype distinctions in a very efficient manner. Approaches have also been developed to classify, annotate or generate biological image data and analyze complex interactions between biological objects from a large set of experiments. The applications of this work range from basic research in biology to drug discovery and diagnostics with identified collaborators in these fields.

In this project, we aim to develop an image analysis system that will make it possible to perform a diagnosis of Malaria infection. We particularly wish to extend the classical deep learning approaches to this issue by using attention mechanisms to improve the accuracy, robustness and reliability of existing solutions. This project will benefit from the expertise of our collaborators at the IRD and AP-HP in the diagnosis of malaria.

Activities

The selected candidate will join our laboratory and engage in research work to design and develop deep learning methods on large datasets of microscopy images. He / she will interact with the other members of the laboratory to develop his knowledge and his know-how of deep learning techniques for the benefit of his project. This could take the form of an idea, a proposal for a digital experience based on data, algorithms or network architecture. He / she will work with our Malaria specialist partners to understand and possibly influence data acquisition and discuss results. He / she will also be responsible for writing and submitting manuscripts describing new quantitative methods validated by scientific results to conferences in machine learning or pattern recognition and to international journals in the field with a referee.

Skills

The candidate must:
- hold a master's / engineer / doctorate degree in deep learning / machine, computer science, statistics, applied mathematics or image analysis.
- be strict / herself and organized to carry out its project.
- be able to adapt to the constraints inherent in research projects.
- be able to work and be caring with colleagues, share knowledge and receive advice from other team members.
- be ready to write manuscripts in English. Previous experience related to biology and / or microscopy would be considered a plus but is not mandatory.

Work Context

The ENS Institute of Biology (IBENS) is a fundamental research center that conducts original research aimed at deciphering the fundamental mechanisms at the heart of biological processes.

A joint ENS-CNRS-INSERM unit, IBENS welcomes more than 300 people grouped into 30 independent teams leading highly collaborative and multidisciplinary research that combines experimental and theoretical approaches. The research activity covers various thematic fields: Computational Biology, Neurosciences, Developmental biology, Functional genomics, Ecology and evolutionary biology.

The reception team (computational bioimaging and bioinformatics :https://www.ibens.ens.fr/spip.php?rubrique47), is led by Auguste Genovesio and includes around 12 computer scientists. The team is part of the Center for Computational Biology, an interdisciplinary and international research center located at IBENS. The team conducts and publishes computer science research to design methods typically using a large dataset of biological images. Our work is currently focused on the development of deep learning methods for the quantitative study of cell morphology and dynamics from large scale data.

The Ecole Normale Supérieure is a public research and higher education institution, located in the Latin Quarter in central Paris, close to many public transportation (RER, subway, bus) and in pleasant and student environment.

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