OPEN COMPETITIONS CR-2024-GA-3
Junior Research Scientist in statistical learning and artificial intelligence for animal genetics
78350 JOUY-EN-JOSAS
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INRAE presentation
The French National Research Institute for Agriculture, Food, and the Environment (INRAE) is a public research establishment under the dual authority of the Ministry of Agriculture and the Ministry of Research.
INRAE is recruiting researchers by open competition and offering permanent position.
It is a major player in research and innovation created on 1st of January 2020. INRAE is a research institute resulting from the merger of INRA and IRSTEA. It is a community of 12,000 people with more than 200 research units and 42 experimental units located throughout France.
The institute is among the world leaders in agricultural and food sciences, in plant and animal sciences, and is 11th in the world in ecology and environment. INRAE’s main goal is to be a key player in the transitions necessary to address major global challenges. In the face of the increase in population, climate change, scarcity of resources and decline in biodiversity, the institute develops solutions for multiperformance agriculture, high quality food and sustainable management of resources and ecosystems.
Work environment, missions and activities
You will join the Animal Genetics and Integrative Biology joint research unit (GABI, 115 permanent staff), whose research aims to understand and exploit the genetic variability of domestic animals to facilitate the agro-ecological transition of livestock farming. Within this unit, you will join the GIBBS "Genomics, Biodiversity, Bioinformatics, Statistics" team, in which several researchers specialize in the analysis of complex and heterogeneous “omics” data. To develop your research work, you will share your time between this team and the SOLsTIS "Statistical modelling and Learning for environment and life Sciences" team in the Mathématiques et Informatique Appliquées Paris-Saclay unit (MIA Paris-Saclay, 40 permanent staff), whose research 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 lead methodological development projects in data science, artificial intelligence and statistical learning to analyze complex, heterogeneous and high-resolution phenomic data from precision agriculture. You will design new methods or improve existing approaches to integrate these multiple data (high-throughput and continuous measurements of various traits, images, sensor recordings, multi-omics data from the host and/or its microbiota, environmental data, etc.). Your work will aim to quantify, characterize and predict key traits for the agro-ecological transition of livestock farms and their adaptation to climate change (resilience and various adaptive capacities contributing to animal welfare, reduced environmental impact, etc.). You will benefit from the scientific environment of both the GIBBS and SOLsTIS teams, and reinforce their joint research dynamics, organized in particular around the analysis of heterogeneous, large-scale data with a view to better understanding and characterizing the genetic and environmental variability of domestic animal phenotypes.
You will work closely with GiBBS scientists, who have expertise in multivariate data analysis -omics and network inference, and SOLsTIS scientists, who will contribute their skills in statistical and deep learning algorithms, open to artificial intelligence, machine learning and corresponding optimization methods. You will also interact with GABI unit geneticists in the design and development of research projects, and in the interpretation of their results. As soon as you arrive, you will be involved in ongoing projects related to animal welfare and traits for adapting animals to different environments. You will have access to INRAE's computing platforms.
In line with INRAE guidelines for Open Science, you will promote your research work to the scientific community through publications and create R / Python packages in order to allow a wide distribution of the developed methods. You will have access to the computing clusters of the INRAE.
You will rely on the existing network of collaborations established by the GiBBS and SOLsTIS teams and expand it at different levels: local, national, and international. You will intervene in training (masters, research schools) and supervision of interns and doctoral students.
Our research team tells you more about your future job
Training and skills
PhD degree (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).
Fluent English speaking and writing is recommended, as well as a long-term international experience.
The selected candidates who have not already such experience will be required to spend a period abroad at the end of the internship year.
INRAE's life quality
By joining our teams, you benefit from:
- 30 days of annual leave + 15 days "Reduction of Working Time" (for a full time);
- parenting support: CESU childcare, leisure services;
- skills development systems: training, career advise;
- social support: advice and listening, social assistance and loans;
- holiday and leisure services: holiday vouchers, accommodation at preferential rates;
- sports and cultural activities;
- collective catering.
For international scientists: please visit your guide to facilitate your arrival and stay at INRAE.
All persons employed by or hosted at INRAE, a public research establishment, are subject to the Civil Service Code, particularly with regard to the obligation of neutrality and respect for the principle of secularism. In carrying out their functions, whether or not they are in contact with the public, they must not express their religious, philosophical or political convictions through their behaviour or by what they wear. > Find out more: fonction publique.gouv.fr website (in French)