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Olivier Martin

Responsable éditoriale :
Rozenn Le Guyader

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Thierry Balliau

A postdoctoral position in Biostatistics is open at GQE-Le Moulon

Maize is a plant of major agronomic interest and a better understanding of its adaptation to environmental conditions is of paramount importance in the context of global climate changes and sustainable agriculture.

Our research group generated plant material from two Divergent Selection Experiments (DSEs) for flowering time that started in 1997 from two maize inbred lines. During 20 years, the earliest and the latest plants were reproduced by selfing generating derived populations of Early- and Late-flowering genotypes (Durand et al. 2010, Durand et al. 2015). Over the 20 years, flowering time and plant height data were collected on the selected generation and ancestral lines and climatic data of the field were recorded.

More recently, progenitors derived from different generations of selection were grown and characterized in the field during two to three years of experimentation. Phenotypes measured and these genotypes in different environments demonstrate significant effects of the environment and GxE interactions.

This project aims at understanding the effect of environment on the plant phenotypes. The postdoc will (i) work on the identification of non-redondant informative climatic variables for the study of maize phenotypes, (ii) deciphering the effects and interaction of climatic variables on kin maize genotypes and (iii) study if/how these effects can differ depending on the genotype using statistical approaches. Taking advantage of the numerous environmental contexts, ontology-based methods will also be developed by a computer science PhD student in the frame of this project in order to identify semantic rules governing plant responses to the environment. Validation of the results will be possible through the confrontation of both approaches.

We are looking for a postdoc, doctor in biology interested in plant development, adaptation and genotype / environment interactions, with a strong experience in statistical modelling applications in biology. An expertise in manipulating big datasets and programming (R and/or python) is required.

At the UMR GQE-Le Moulon, he/she will directly benefit from the expertise of people directly involved in the project that generated the data (Saclay DSE, E. Marchadier, C. Dillmann and A. Ressayre). Moreover, he/she will work closely with computer scientists (Fatiha Saïs and Nathalie Pernelle, LRI – University Paris Sud and Juliette Dibie, MIA-Paris INRA & AgroParisTech) and a statistician (Jessica Tressou, MIA-Paris INRA). Finally, he/she will collaborate with Japan experts on rice.

Duration: 1 year and 6 months funded by the institute of convergence DATAIA1

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