Postdoc (2 years + 1) - Computational Network Biology/Machine Learning

 CDD · Postdoc  · 24 mois (renouvelable)    Bac+8 / Doctorat, Grandes Écoles   CANTHER CNRS UMR 9020 - Inserm UMR 1277 · Lille (France)

 Date de prise de poste : 1 septembre 2021


Computational Network biology Machine Learning Automation of Scientific Research XAI open-source bioinformatics software


The research carried out will leverage and support the I3-BioNet concept, which combines inference, interrogation and intervention with co-regulatory networks. The concept is particularly illustrative and interesting and paves the way to a research which goes beyond the simple analysis of biological data, by targeting the biological system in its entirety. We combined ML software (for data analysis, model formation, experimental design), with network biology software (bioinformatics, systems biology modelling) and visualization to execute a cycle of systems biology model development. A second challenge is to facilitate the collaboration with scientists to accelerate systems biology models development. AI systems have super-human scientific powers: they can learn from vast amounts of data, execute error-free logical reasoning, execute near optimal probabilistic reasoning, coordinate the parallel testing of different hypotheses; and only scientists have the knowledge and experience to direct these experiments into profitable areas of research. This post-doc position is funded by ANR as part of the FNR INTER/ANR PRCI GREENER project.

For further information on the topic, see: - A. Coutant, K. Roper, D. Trejo-Banos, D. Bouthinon, M. Carpenter, J. Grzebyta, G. Santini, H. Soldano, M. Elati, J. Ramon, C. Rouveirol, L. Soldatova, R. D. King. Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast. PNAS,116 (36) 18142-18147, 2020.

- Zerrouk, N., Miagoux, Q., Dispot, A., Elati, M., & Niarakis, A. (2020). Identification of putative master regulators in rheumatoid arthritis synovial fibroblasts using gene expression data and network inference. Scientific reports, 10(1), 1-13.

- W. Dhifli, J. Puig, M. Elati. Latent network-based representations for large-scale gene expression data analysis. BMC Bioinformatics, 2019.

- P. Trébulle, J-M Nicaud, Ch. Leplat, M. Elati, Inference and interrogation of a coregulatory network in the context of lipid accumulation in Yarrowia lipolytica NPJ Systems Biology and Applications (2017): Aug 11;3:21. eCollection 2017.

- R. Nicolle, F. Radvanyi, and M. Elati, CoRegNet: reconstruction and integrated analysis of co-regulatory networks, Bioinformatics, btv305, 2015.

- Elati M, Neuvial P, Bolotin-Fukuhara M, Barillot E, Radvanyi F, Rouveirol C. LICORN: learning cooperative regulation networks from gene expression data. Bioinformatics 2007; 23:2407-14. 


Procédure : Interested candidates are encouraged to submit a CV, contact details of two references and a short statement of research interest electronically to Prof. Mohamed Elati ( To be assured of full consideration, applications must arrive by July 31, 2021. Please feel free to contact us for informal inquiries and additional information.

Date limite : 31 juillet 2021


Mohamed Elati

Offre publiée le 23 juin 2021, affichage jusqu'au 31 juillet 2021