Postdoctoral position in machine learning

 CDD · Postdoc  · 36 mois    Bac+8 / Doctorat, Grandes Écoles   UMR 1332 BFP · Villenave d'Ornon (France)  Gross salary by month: approx. between 2500 and 2900, depending on previous experience

 Date de prise de poste : 1 septembre 2021

Mots-Clés

Modélisation du métabolisme machine learning Cameline

Description

Research unit
Currently counting over 150 staff members including non-permanent personnel, UMR 1332 BFP is a partnership between INRAE (Divisions of Biology and Plant Breeding and Plant Health and Environment) and the University of Bordeaux. It constitutes a major pillar of plant biology research in Nouvelle- Aquitaine. In parallel to its research activities, BFP has a strong involvement in teaching and training in the Plant Sciences field but also in Microbiology and Biochemistry. It has strong partnerships and high international visibility, as indicated by the coordination of several EU-funded projects or collaborative networks, the establishment of an International Associated Laboratory with the University of Tsukuba in Japan (LIA FReQUenCE) and participation in several international projects. BFP also shows strong partnerships in several areas with the industry or with grower organisations.

The Meta team is a multidisciplinary team (analytical chemistry, biochemistry, molecular biology, physiology, statistics and bioinformatics) using Systems Biology approaches to understand metabolism and the way it participates in the performance of plants, whether in terms of yield or adaptation to abiotic and biotic stress. The team generates large datasets to develop, parameterise and validate predictive models. Bottom-up mathematical models (from mechanisms to the phenotype) are used to understand how the fluxes and concentrations of metabolites are controlled. Top-down models (from the phenotype to mechanisms) are used to look for metabolic markers associated with performance, the first step towards identifying the underlying mechanisms. While in recent years the team has focused on central metabolism, now it is also interested in redox metabolism as well as secondary metabolism, in particular because of their involvement in adaptation to the environment. The Meta team also hosts the Bordeaux Metabolome Platform that enables a wide range of targeted and untargeted metabolomics.

Context
The GLOMICAVE project develops a new digital genotype-to-phenotype platform, relying on Big Data Analytics and Artificial Intelligence and using large-scale publicly available and experimental omic datasets. The project addresses the need to build systems allowing the use of primary data analytical processes and support large-scale omics experiments, thus maximising the utility of omic data at a massive level and understanding biological systems as a whole.

The Meta team is leading WP4 (“AI-based genotype-phenotype unknown relationship discovery”), which goal is to benefit from comprehensive, multi-omics datasets and predict novel relationships between genotypes and phenotypes that relate to diverse fields of life sciences. More specifically, this WP aims at providing robust systems-based tools for predictive integration of multi-omics and multi-object data and predicting and/or classifying phenotypic traits of interest using systems-based approaches.

Objectives and work program of the post-doctoral fellow

The postdoctoral fellow will perform top-down modelling approaches. She/He will use large-scale omic datasets produced in the project to predict complex phenotypic traits using machine-learning methods. Both supervised and unsupervised techniques will be deployed depending on the type of phenotypic traits to predict.

Besides computational biology, the post-doctoral fellow will have the opportunity to generate metabolomic data for various fruit species using state-of-the-art metabolomic instruments at Bordeaux Metabolome Facility. More particularly, untargeted LCMS-based metabolomics, targeted GCMS profiling and biochemical phenotyping will be used to evaluate a broad spectrum of plant metabolites ranging from primary to secondary compounds, also including redox molecules. This unprecedented metabolite dataset will be exploited to reveal key aspects of plant performance.

The post-doctoral fellow will be involved in a modeling group, within the Meta team, made up of several researchers, another post-doctoral fellow over the same three years period, and phD students.

REQUIRED QUALIFICATIONS

Candidate skills: Experience in machine learning and biological modelling is essential. Knowledge in metabolomics and/or other omic techniques would be a valuable asset. The candidate must be able to work in a network and to report on her/his results. She/he must also demonstrate good levels of oral and writing English and skills in drafting science publications.

Candidature

Procédure : Send a motivation letter and CV to: Pierre Pétriacq: pierre.petriacq@inrae.fr + 33 (0) 5 57 12 25 75 Sylvain Prigent: sylvain.prigent@inrae.fr +33 (0)5 57 12 25 40 Yves Gibon: yves.gibon@inrae.fr + 33 (0) 5 57 12 26 51

Date limite : 31 mai 2021

Contacts

Pierre Petriacq

 piNOSPAMerre.petriacq@inrae.fr

 http://sylvainprigent.fr/postdocPositionGlomicave_UMR1332.pdf

Offre publiée le 26 avril 2021, affichage jusqu'au 31 mai 2021