CIRAD’s AGAP Institute, a joint research unit focusing on the Genetic Improvement and Adaptation of Mediterranean and Tropical Plants, brings together a wide range of skills, approaches and resources in order to develop cultivated plants that are better adapted to a variety of agrosystems. More effective tools for predicting genetic values, in particular those able to integrate complex, non-linear relationships using heterogeneous imbalanced data (genomic, environmental, etc.), will make the breeding schemes conducted with our partners more effective and will help, in particular, with the issue of climate change adaptation.

In this context, CIRAD is recruiting a researcher (M/F) specialising in deep learning and other machine learning methods, in order to adapt them and apply them to plant improvement, with the aim of better predicting genetic values and more effectively identifying the factors controlling phenotypic variability (1, 2).

You will join AGAP’s “Génome et Sélection des plantes Pérennes” (GSP) team in Montpellier, which boasts recognised expertise in identifying the genetic mechanisms controlling traits of agronomic interest, characterising and developing genetic resources, and optimising breeding strategies for major tropical perennial crops (oil palm, cocoa, rubber, etc.).

You will be responsible for conducting the following activities:

• research into genomic prediction and identification of causal polymorphisms: methodology watch, identification and application of suitable approaches, comparisons between deep learning, other machine learning methods and conventional approaches (biostatistics/quantitative genetics), and theoretical developments and/or adaptation of existing methods. Initially, you will take part in ongoing research projects (genomic selection of oil palm), following which you will focus on setting up projects in collaboration with other AGAPi researchers. You will be involved in student supervision and scientific promotion (articles, conferences).

• skills transfer, via in-house training and scientific events within AGAP, and training for partners in the Global South

You will be able to draw on the support and experience of researchers in quantitative genetics, mathematics/biostatistics, computer science, data science, machine learning, agronomy, etc. within both AGAP and other CIRAD units. You will be expected to develop collaborations with the University of Montpellier, CNRS, INRAE and abroad.

Education:

You have a PhD in data science with an interest in biology, or a PhD in biology with a specialisation in data science.

Required skills and know-how:

. Expertise in neural networks and other machine learning and artificial intelligence methods

. Solid command of computer programming (Python, Bash) and using calculation servers (SLURM), advanced knowledge of the Scikit-Learn, Pytorch and Tensorflow libraries

. Good oral communication and scientific writing skills in English

Desired skills:

. Knowledge of biostatistics, bioinformatics, quantitative genetics and/or genetic improvement methods

. Leadership skills

. Experience in teaching, training and/or supervising students

. Experience in setting up projects

Personal skills:

. Interpersonal skills

. Interest in interdisciplinary work

. Dynamism, creativity and initiative

Please include in your CV the names of thesis supervisors and managers from periods of professional experience whom we can contact for a reference.

Benefits: You will receive a 13th month’s salary. To find out more: Our strengths

In the medium term, your activities should enable you to obtain an HDR (Habilitation to Supervise Research).

1 Crossa et al. 2024. Machine learning algorithms translate big data into predictive breeding accuracy. Trends in Plant Sci.

2 Novakovsky et al. 2023. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat. Rev. Genetics