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Reference : UMR5175-FRAVAS-002
Workplace : MONTPELLIER
Date of publication : Wednesday, September 8, 2021
Type of Contract : FTC Technical / Administrative
Contract Period : 12 months
Expected date of employment : 1 December 2021
Proportion of work : Full time
Remuneration : Between 2019 and 2228€ depending on experience
Desired level of education : 5-year university degree
Experience required : Indifferent
Modern agriculture is currently faced with multiple challenges to maintain or even increase crop yields. The creation of hybrids in many species is a common solution that aims to produce homogeneous varieties that maximize yield, taking advantage of the phenomenon of hybrid vigor, or heterosis. However, it remains difficult to understand the mechanistic basis of heterosis, as well as to predict its amplitude in different species. In this perspective, the engineer recruited will engage in a promising but still little explored approach consisting in relying on mathematical models developed in comparative ecology to link functional traits related to resource acquisition and use to plant performance and yield.
The engineer hired will extract data on hybrid performance in crops from the scientific literature, organize these data in a database, and analyze variation in hybrid vigor between species and genotypes. Specifically, the engineer will examine non-linear relationships between traits based on functional ecology models, and test whether these relationships can explain a significant portion of heterosis in crop species.
- ability to structure, organize and analyze large data sets
- mastery of statistical analysis software (R)
- ability to work in a team
- fluency in English (written and spoken)
- knowledge of cultivated plants and the phenomenon of heterosis
The recruited engineer will be located at the Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), on the CNRS campus in Montpellier. He/she will be mainly supervised by François Vasseur, within the ECOPAR team.
Constraints and risks
No constraints or risks identified.
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