Computational and Machine Learning-based Methods in Phylogenetics

 CDD · Postdoc  · 12 mois (renouvelable)    Bac+8 / Doctorat, Grandes Écoles   Institut de Systematique, Evolution, Biodiversité (UMR 7205 CNRS, MNHN, UP, EPHE, UA) · Paris (France)  2750€ to 3900€ before tax per month, depending on experience (CNRS salary scale)

 Date de prise de poste : 1 février 2022


Phylogenetics machine learning algorithms statistical model design and selection phylodynamics tree-of-life big data



In the context of the PRAIRIE program (, the postdoctoral fellow will work at the intersection of machine learning, genomics and evolution, under the supervision of Olivier Gascuel ( Recent publications have shown the potential of machine learning methods and advanced computational techniques to tackle key issues in phylogenetics and phylogenomics. The research framework spans a wide range of topics, in particular: statistical modeling of evolutionary processes, selection/adequacy of evolutionary models, non-parametric approaches, statistical significance and uncertainty of evolutionary inferences, fast inference algorithms, large-scale phylogenetics and phylogenomics, phylodynamics, analysis of viral data, biodiversity assessment.


The postdoc fellow will work on one of the following subjects:
* Machine leaning-based non-parametric modeling of evolutionary processes (e.g. substitution, indels, virus spread, species diversification).
* Machine learning and computational statistics methods to compare and select evolutionary models.
* Design and evaluation of branch-support methods for gene and species trees.
* Algorithm design and implementation for large-scale phylogenetics and phylogenomics.
The applicants are welcome to propose variants along these lines, for example combining modeling and model selection, or tree inference and branch support. In a first step, the subject will be fully defined and planned with the supervisor. Then, he/she will work in close interaction with him, notably to write publications intended to computer science, bioinformatics or evolutionary biology journals.


The candidate should:
* Be computer-science oriented with skills and experience in machine learning and/or computational statistics and/or algorithm design and analysis.
* Have strong programming skills (Java, C, Python, etc.).
* Have capacity to work in a collaborative and interdisciplinary environment.
* Possess written and oral communication skills.


The host lab is the Institut de Systématique, Évolution, Biodiversité (ISYEB – UMR7205), at the Muséum National d'Histoire Naturelle (Paris). The work will be carried out in the BIPEM team (Biologie Intégrative des Populations et Evolution Moléculaire) in close interaction with the ABI team (Atelier de Bioinformatique). The postdoc fellow will be interacting with other researchers and will collaborate with external teams.

The initial duration of the postdoctoral contract will be 1 year. An extension of the contract to 2 years will be possible depending on satisfactory results. The contract can begin in January-February 2022, depending on paper work and administration. The salary is determined by the CNRS salary scale for postdocs, which takes into account the experience of the candidate.


Procédure : To apply, please submit on the CNRS URL: a CV; a motivation letter, a project summary (~1 page); the email contact of 2-3 referrers (PhD supervisor etc.). Any prior request by email is welcome.

Date limite : 1 février 2022


Olivier Gascuel

Offre publiée le 9 décembre 2021, affichage jusqu'au 1 février 2022