Post-Doctoral Research Visit F/M Inference of a demo-genetic model for sustainable plant resistance

The Inria centre at Université Côte d'Azur includes 42 research teams and 9 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur  is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

This postdoctoral position if funded by the ANR (French National Research Agency)   ENDURANCE ENhanced DURability AgaiNst Crop Enemies project, which brings together partners from INRAE and Inria. This interdisciplinary project combines molecular biology, population genetics, and epidemiological modelling, to determine optimal deployment strategies for plant resistance.

The postdoctoral fellow will join the  MACBES team (Inria, INRAE, CNRS, Université Côte d'Azur) in Sophia Antipolis and will closely collaborate with  Suzanne Touzeau (MACBES & M2P2 teams) and Florence Carpentier (AgroParisTech & MaIAGE, INRAE) based near Paris.

The postdoctoral fellow will interact with other ENDURANCE partners and the  M2P2 team at ISA (INRAE, CNRS & Université Côte d'Azur).

Crop protection often remains dependent on chemical pesticides, which are both harmful for the environment and human health. Resistant crops are an agroecological alternative to pesticides, but their extensive use may lead to the emergence/selection of virulent pathogens and resistance breakdown. Devising deployment strategies of resistant crops that are both efficient, i.e. that reduce crop damages, and durable, i.e. that limit the virulent pathogen populations, is hence a major issue.

The postdoctoral fellow will tackle this issue by means of a demo-genetic model, tailored for a specific pathosystem, the phoma stem canker of oilseed rape caused by fungus Leptosphaeria maculans. The emergence and development of virulent pathogens may vary according to the genetic determinisms of virulence (molecular mechanisms responsible for the transition to virulence, epistatic interactions, fitness costs), which are studied by other partners of the ENDURANCE project.

The work will be based on:

• time-series data of (i) phoma populations and resistance breakdowns, as well as (ii) resistance deployment in oilseed rape crops;
• a stochastic, discrete-time epidemiological model of an haploid monocyclic fungal pathogen, which includes features of the oilseed rape stem canker, such as interactions between resistance and avirulence genes;
• the corresponding C++ code.

The objectives of this position are threefold:

1. adapt the model to take into account migration, mutation, and pathotype-dependent virulence costs, based on recent advances in the genetic determinisms of virulence;
2. develop a method to estimate model parameters from historical data, in order to gain deeper insights into the observed dynamics of resistance breakdown;
3. devise durable strategies for the deployment of multiple resistances.

Generic activities include: literature review, data processing, reporting, paper writing, participation and presentation in project meetings and in relevant conferences.

Specific activities include:

• dynamical model development,
• programming and numerical simulations (using a computing cluster),
• inference based on simulations (ABC-like method),
• numerical exploration (sensitivity analysis) and optimisation.

• Background in population dynamics and/or population genetics.
• Expertise in inference and/or optimisation.
• Experience in programming, preferably in C++.
• Knowledge of plant epidemiology would be a plus.
• Proficiency in written and spoken English.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Contribution to mutual insurance (subject to conditions)

2788 € per month