Open competitions : CR INRAE 2023

Open competitions for research scientists at INRAE 2023 : GABI is recruting two deux Junior Research Scientists

You would like to contribue to public research, to get involved in projects important for society's challenges ? Join our research teams by enrolling in one of our Open competitions for a Junior Research Position (men/women) : applications open up to March 2, 2023!

Who may apply?

This competition is the gateway to civil service and allows access to permanent positions. Research fellows are generally recruited from among researchers at the beginning of their careers who have defended a thesis (or who can prove that their scientific qualifications and work are equivalent). You must have published the results of your thesis. You will be recruited for your scientific skills, which you will put to the service of INRAE's major orientations in order to respond to a specific research theme. You can find out about this theme in the job profiles that are online.

Applications are open until Marche 2nd!

A Junior Research Scientist in epidemiological genetics for farm animal health

Within GABI, you will join the GeMS team: "Genetics, Microbiota, Health" (7 researchers, 2 engineers, 5 technicians). The team studies the combined impact of host genetics, intestinal microbiota, and immunity and their interactions on pig and chicken health. The team studies the genetic determinism of resistance to specific pathogens and develops integrative biology approaches to identify predictive biomarkers or signatures of good health that can be used on farms and/or for selection. You will broaden the current approaches by developing a new genetic epidemiology strategy. You will also contribute to the study of the genetic architecture of immune and immunocompetence traits.

In outdoor and cage-free breeding systems, currently under development, animals are more exposed to environmental constraints, pathogen pressure may be greater and contact between animals is more frequent. These multiple host-host and host-pathogen interactions make health management more complicated. Until now, health improvement strategies only considered individual animal responses to infections. You will first contribute to renewing concepts by developing new models in genetic epidemiology, taking into account these interactions and the infection dynamics, in addition to individual genetic variability.

You will first develop your research on a “chicken-coccidiosis” model, a major parasitic disease in poultry, using available data on individual responses (genetic markers, gene expression levels, vaccine responses). You will design experiments necessary to validate your model and estimate the genetic parameters of traits related to infectiousness and the ability to transmit disease. The integrative analysis of the new data generated will improve understanding of the genetic architecture of the traits studied. In a second stage, you will expand your research to other pathogens of interest in poultry and/or pig production, and develop your research to contribute to the development of new selection strategies for improving animal health and for an efficient agroecological transition, in collaboration with professional actors in the field. You will benefit from the team's expertise in computational biology, immunophenotyping, and genomics, as well as that of the unit's researchers in bioinformatics and biostatistics. You will benefit from a dynamic network of inter-disciplinary collaborations within INRAE (especially Animal Health division), with ANSES, INSERM, and well-established partnerships with professional organizations in the avian and porcine sectors. You will have access to several infrastructures (resource centres, technological platforms, and experimental units). You will join a collaborative network in modelling within the Animal Genetics Division, and you will develop your own network at INRAE and international level with recognized research teams in this field.

Training and skills

Candidates must have a PhD or equivalent.
A PhD (or equivalent) in quantitative genetics is highly recommended, as well as a strong interest in modelling of biological data, genetic epidemiology, heterogeneous data integration, and the genetic determinism of health traits. Knowledge in health and/or immunology and/or epidemiology would be highly desirable.

Candidates should have a good command of English (both written and spoken), and long-term international experience would also be desirable. Successful candidates who have not yet acquired this experience abroad will be required to do so after their probationary period (1st year).

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Junior research scientist in statistical learning and artificial intelligence for biology

You will join the INRAE Animal Genetics and Integrative Biology joint research unit (GABI, 130 permanent staff members, “Animal Genetics” GA Division), whose research aims to understand and exploit the genetic variability of domestic animals in a context of agroecological transition. Within this unit, you will join the GiBBS "Genomics, Biodiversity, Bioinformatics, Statistics" team bringing together several researchers specializing in the analysis of complex and heterogeneous “omics” data. To develop your research project, you will share your time between this team and the SOLsTIS “Statistical modelling and Learning for environmenT and lIfe Sciences” team in the MIA-Paris-Saclay joint research unit (Mathematics and Applied Computer Science-Paris, 32 permanent staff members), part of the MATHNUM (Mathematics and Digital Science) division, located on the Saclay site (Palaiseau). 

The research by MIA-Paris aims to develop advanced statistical and computer science methods for specific problems in the life sciences. The SOLsTIS team includes several researchers who are experts in statistical learning algorithms, including artificial intelligence. You will conduct methodological developments in data science, artificial intelligence, and statistical learning for the genetic and genomic analysis of traits linked to the agroecological transition of breeding farms and their adaptation to climate change (resilience, animal welfare and adaptation, reduction of environmental impact) and the promotion of genetic diversity. You will benefit from the scientific environment and the support of the statisticians of the two teams GIBBS and SOLsTIS, and will thus strengthen their common research dynamic, organized in particular around the analysis of heterogeneous and large-scale data to improve understanding and characterization of domestic animal phenotypes. You will design new methods or improve existing approaches for integrating heterogeneous data (multi-omics, high-throughput and continuously collected measurements, images, captor recordings, and geolocation data). You will work in complementarity with GiBBS scientists, who have expertise in multivariate analysis and network inference, and the SOLsTIS scientists, who will bring their skills in artificial intelligence and machine learning algorithms. Upon arrival, you will be involved in two projects related to animal welfare and animal adaptation to different environments. You will interact with geneticists from the GABI Unit and the GA department during the design and development of research projects and for the interpretation of their results. In line with INRAE guidelines for Open Science, you will promote your research work with the scientific community through publications and make R/Python packages available to allow the wide distribution of the methods developed. You will have access to the computing clusters of the INRAE sites in Jouy-en-Josas and Toulouse (Migale, Genotoul). You will draw on the existing network of collaborations established by the GiBBS and SOLsTIS host teams and expand it at different levels: local, national, and international. You will participate in teaching (masters, research schools) and supervision of interns and doctoral students.

Training and skillsCandidates must have a PhD or equivalent.
A PhD (or equivalent) in Biostatistics / Applied Mathematics / Artificial Intelligence, with excellent training in statistical learning and data science, a strong interest in modeling biological data, as well as very good programming skills (R / Python) are highly recommended.

Candidates should have a good command of English (both written and spoken), and long-term international experience would also be desirable. Successful candidates who have not yet acquired this experience abroad will be required to do so after their probationary period (1st year).

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Modification date : 03 February 2023 | Publication date : 02 February 2023 | Redactor : INRAE - Edition P. Huan