Postdoctoral fellowship: Integrative analysis and modelling of the major determinants of feed efficiency using longitudinal data from dairy cows
Deadline for applications
Date of publication


Details on the type of contract
Postdoctoral contract
Duration of contract
24 month

2 300 €/month (depending on the experience )


Name of unit of assignment
UMR1313 GABI Génétique Animale et Biologie Intégrative
Address of unit of assignment
INRA Domaine de Vilvert - 78352 JOUY-EN-JOSAS CEDEX
Website of unit of assignment -
Region of assignment
Paris Region


Working environment

Description de l’unité et de l’équipe d’accueil : The postdoc fellow will work in two teams located in Paris and at Jouy-en-Josas (30 km from Paris) : UMR 791 MoSAR Modélisation systémique appliquée aux ruminants 16 rue Claude-Bernard, 75005 Paris, UMR1313 GABI Génétique Animale et Biologie Intégrative Domaine de Vilvert 78350 Jouy-en-Josas.


Missions et activités confiées :  the postdoctoral fellow will join an interdiscisciplinary project (funded by ANR and APIS-GENE) called Deffilait with the title “Improving feed efficiency in dairy cows: understanding its key determinants using precision phenotyping, to allow tailored genetic selection strategies according to environment.” To study the major determinants of feed efficiency, the project will build an original database of dairy cow lactations with a large set of phenotypes to describe the main sources of energy transformation, thus explaining the observed between-animal variability in feed efficiency. This dataset will then be used to quantify the contribution of the different mechanisms to the variability in feed efficiency, and to test different indicators and strategies to improve feed efficiency. A specific focus will be made on body reserves mobilization in early lactation to assess its genetic components and correlation with other traits with a larger dataset involving commercial farms. The project will then develop simulation tools to predict the short- and long-term consequences of different selection strategies in different environments. The expected results will contribute to the definition of strategies of selection to combine efficiency and robustness. The project will provide a coherent framework to undertake a balanced genetic selection on these traits, and thereby make a significant - and lasting - contribution to improving feed efficiency.

The main mission of the postdoc will be to develop the statistical and mathematical tools for the analysis of the multivariate time-series data describing trajectories of performance and body reserves through lactation, combine these with genotypic and phenotypic information relating to health, reproductive success and productive longevity, and to integrate them into a modelling framework that will help identify the genetic drivers of long-term feed efficiency. This modelling work will have to combine dynamic modelling components with quantitative genetic approaches.  The resulting models will then allow simulation of the consequences on long-term efficiency of selection strategies targeting different performance traits.


Site Web de l’unité :

Training and skills required

Formation recommandée : Thesis in biology or applied maths/stats Connaissances souhaitées : Expertise in time-series statistics (DLM, FDA, etc), including in a multivariate context, and dedicated software Expérience appréciée : A good knowledge of biological systems. Previous experience in quantitative genetics will be a plus and/or in dynamic modelling (ODE, parameter estimation, sensitivity analysis) Aptitudes recherchées :  Excellent communication skills to interact in an interdisciplinary environment involving geneticists, animal scientists and modellers.  Capacity to extract biologically meaningful knowledge from mathematical developments and willingness to learn tools for modelling dynamic systems.



Transmettre une lettre de motivation, un CV et les noms de deux référents scientifiques à Nicolas Friggens ET Didier Boichard

Coordonnées e-mail :


Didier Boichard / Nicolas Friggens