We are seeking a highly motivated postdoctoral fellow to join our research team at the laboratory Quantitative Genetics and Evolution – Le Moulon (GQE), located on the Paris-Saclay University campus, in the southern vicinity of Paris. The successful candidate will be part of the vibrant scientific environment of the Saclay Plant Science (SPS) community.
Project Overview and Responsibilities
The postdoctoral fellow will participate to the ANR JCJC NETWITS project, led by Maud Fagny, which aims at exploring the role of gene regulatory networks structure in maize response to drought. GQE has an important expertise on the molecular bases of maize drought response, including association studies and the inference of gene regulatory networks. We thus have identified numerous loci, both genes and regulatory elements, potentially involved in determining maize yield in response to drought.
The postdoctoral fellow, specialist in quantitative genetics, will develop a yield prediction method for maize in drought condition that will leverage the available information. The aim will be to integrate prior biological information about gene expression regulation and natural selection within the model.
Working with data generated by the members of the NETWITS project and others, the postdoctoral fellow will pursue the following integration steps:
- Classify the polymorphisms into different categories according to their expected importance in the regulatory network, GWAS, eQTL and population genetics analyses.
- Use bio-informed methods such as (Bertolini 2025), GFBLUP (Edwards 2016, Fang 2017), or BayesRC+ (Fikere 2018, Mollandin 2022) to directly integrate the regulatory network based classification in the model.
- Eventually implement a bio-informed neural network to directly integrate the regulatory interactions in the model (NetGP, Zhao 2025; DLGBLUP, Shokor 2025).
The predictive abilities of these models will be compared to reference models such as GBLUP in different prediction scenarios potentially involving genotype x environment interactions. For this, the postdoctoral fellow will design cross-validation scenarios based on a multi-environment trial of 250 maize hybrids evaluated in 25 environments. They will particularly focus on the prediction of genetically distant material. All datasets are already curated and ready to use.
The postdoctoral fellow will be supervised by M. Fagny (GEvAD), Renaud Rincent and Tristan Mary-Huard (GQMS). They will collaborate closely with the other participants of the NETWITS project, in order to integrate their results in the model. The postdoctoral fellow will also be responsible for supervising interns (licence or master students) and to help training the PhD students of the team in quantitative genetics.
The postdoctoral fellow will be welcomed in the GEvAD team (Evolutionary Genetics and Crops Adaptation, http://moulon.inrae.fr/en/equipes/gevad/) at the UMR Quantitative genetics and Evolution (GQE) – Le Moulon (Gif-sur-Yvette, France). GQE is part of IDEEV (the Institute for the Diversity, Ecology and Evolution of the Living World, https://www.ideev.universite-paris-saclay.fr/en/), located on the Paris-Saclay campus. The GEvAD team combines various approaches, including field and greenhouses experiments, theoretical (models, stat development) and applied population genetics, genomics, systems genomics, to understand the evolutionary mechanisms behind the domestication and environmental adaptation of crops. The postdoctoral fellow will also be collaborating with members of the GQMS team (Quantitative genetics and Plant Breeding Methodology), whose research focus on developping experimental and theoretical approaches, statistical methods and decision support tools to understand maize diversity and decipher the architecture of quantitative traits to optimize maize breeding schemes.
Requirements
- The candidate is required to hold a PhD.
- Academic knowledges: Advanced knowledges in quantitative genetics are required; an experience with deep neural network models would be preferred, but not indispensable. Knowledges in systems genomics/gene regulatory networks or in population genetics will be appreciated but are not necessary.
- Bioinformatics: programming skills are required in at least one of the following languages: python or R. Skills in shell and SLURM-based computational clusters will be appreciated. Basic knowledge of the FAIR principles and about git usage are required as all scripts will be developed and made publicly available following the FAIR management standards.
- Communications skills: writing scientific articles, giving poster presentations and talks are required skills to valorize the scientific results. Spoken & written English: B1 to B2 level (Common European Framework of Reference for Languages) is required.
- Interest in supervising students will be appreciated.
Work Environment
- Duration: 12 months (renewable once)
- Full-time, 38.5 hours per week, with the possibility of working remotely twice a week
- Salary: as a postdoctoral fellow, according to INRAE’s salary scale and experience in the field
- Resources available: laptop and calculation servers
- Starting date: Flexible, as soon as possible, ideally before the 1st of April 2026. Applications will be evaluated immediately.
Application
Please send to maud.fagny@inrae.fr a single PDF file including:
- A detailed CV (including publication list and software contributions, if any)
- A cover letter describing your motivation and relevant experience
- (Optional) Contact information for two references
- (Optional) examples of previous work
Bibliography
Bertolini E., et al., 2024. Genomic prediction of cereal crop architectural traits using models informed by gene regulatory circuitries from maize. Genetics 228(4): iyae162. https://doi.org/10.1093/genetics/iyae162
Edwards, S. M., Sorensen, I. F., Sarup, P., Mackay, T. F., & Sorensen, P. (2016). Genomic prediction for quantitative traits is improved by mapping variants to gene ontology categories in Drosophila melanogaster. Genetics, 203(4), 1871–1883. https://doi.org/10.1534/genetics.116.187161
Fang L., et al., 2017. Use of biological priors enhances understanding of genetic architecture and genomic prediction of complex traits within and between dairy cattle breeds. BMC Genomics 18:604. https://doi.org/10.1186/s12864-017-4004-z
Fikere M., et al., 2018. Genomic Prediction Using Prior Quantitative Trait Loci Information Reveals a Large Reservoir of Underutilised Blackleg Resistance in Diverse Canola (Brassica napus L.) Lines. The Plants Genome 11:170100. https://doi.org/10.3835/plantgenome2017.11.0100
Zhao L., et al., 2025. Genomic Prediction with NetGP Based on Gene Network and Multi-omics Data in Plants. Plant Biotechnology Journal 23:1190-1201. https://doi.org/10.1111/pbi.14577
Mollandin F., et al., 2022. Accounting for overlapping annotations in genomic prediction models of complex traits. BMC Bioinformatics 23(1):365. https://doi.org/10.1186/s12859-022-04914-5
Shokor, F., Croiseau, P., Gangloff, H., Saintilan, R., Tribout, T., Mary-Huard, T., & Cuyabano, B. C. D. (2025). Deep learning and genomic best linear unbiased prediction integration: An approach to identify potential nonlinear genetic relationships between traits. Journal of Dairy Science 108(6) :6174-6189. https://doi.org/10.3168/jds.2024-26057