Junior research scientist in integrated modelling of transmission and evolutionary dynamics of pathogens

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INRAE presentation

INRAE, the French National Research Institute for Agriculture, Food, and Environment, is a public research organization bringing together 12,000 employees across 272 units in 18 centers across France. As the world’s leading institute specializing in agriculture, food, and the environment, INRAE plays a key role in supporting the necessary transitions to address global challenges.

Faced with population growth, food security challenges, climate change, resource depletion, and biodiversity loss, INRAE is committed to developing scientific solutions and supporting the evolution of agricultural, food, and environmental practices.

INRAE is recruiting researchers by open competition and offering permanent position.

Work environment, missions and activities

You will join the research unit EPIA (epidemiology of animal and zoonotic diseases), on the VetAgro-Sup campus in Marcy-l’Étoile near Lyon. The unit conducts research on pathogen transmission and evolutionary dynamics in animal populations, drawing on recognised expertise in mechanistic modelling, phylodynamics, and artificial intelligence. These approaches aim to reconstruct transmission chains within structured populations or across species, identify the factors that influence them, predict epidemic trajectories, and assess the effectiveness of surveillance and risk mitigation interventions
As a member of the unit, you will actively contribute to these objectives and collaborate closely with its researchers, engineers, doctoral students, and both national and international partners. You will benefit from a stimulating working environment, including access to high-performance computing platforms, a molecular biology technical platform, and established epidemiological and genomic databases. This position is an integral part of a dynamic, application-oriented research environment, involving close collaboration with stakeholders engaged in the surveillance and management of animal and zoonotic diseases in France and internationally.
Mechanistic models play a central role in the analysis of the transmission dynamics of pathogens in animal populations, as well as the assessment of counterfactual scenarios aimed at identifying the most effective surveillance and control strategies. Nevertheless, the relevance of such models relies on robust parameterisation, which remains largely reliant on incidence data that are often incomplete or biased. Incorporating complementary data sources will help reinforce the reliability of inferences. Among these, genomic data, which retrace the evolutionary trajectories of pathogens, have proven valuable for capturing spatio-temporal transmission patterns through phylodynamic approaches.
However, the joint integration of heterogeneous data and complex mechanistic models often renders traditional inference methods inapplicable. Furthermore, simulation-based approaches require time-consuming adjustments that use considerable computational resources, limiting their use in an operational context in which models must be rapidly updated. In this context, you will explore ways to overcome these technical challenges by developing hybrid models that combine mechanistic modelling, phylodynamics, and machine learning, with the aim to improve the inference of epidemiological parameters and enhance the understanding and prediction of transmission dynamics.
You will develop innovative approaches integrating the epidemiological and evolutionary processes of pathogens within mechanistic modelling frameworks to assess the added value of genomic data for estimating the relative importance of different transmission pathways and for comparing the performance of surveillance and control strategies in relation to inferences based solely on incidence data. To this end, you will design mechanistic models capable of reconstructing transmission chains by comparing model outputs with phylogenetic trees built using genomic data. You will implement inferential algorithms to estimate the model parameters. These approaches will be applied to several case studies for which data are already available, in particular epizootic haemorrhagic disease and avian influenza. In parallel, you will explore the use of deep neural architectures for inference on models of increasing complexity, and you will investigate the identifiability conditions of these models based on the types of data and levels of data aggregation.
This research will fall within the framework of collaborative projects already funded, including the involvement of a postdoctoral researcher for two years. It will lead to the formalisation of an original methodological framework, extended to multiple host systems and various transmission processes. A strategic priority will concern the development of operational pipelines for predicting epidemiological trajectories and assessing counterfactual surveillance and management scenarios. You will also contribute to the dissemination of the developed methods by producing software and open-source packages, facilitating their transfer to scientific and operational communities, and delivering targeted training at INRAE and for international partners.

Training and skills

Competition open to candidates with a PhD (or equivalent).
Recommended training: A PhD in epidemiology, computational biology, statistics, applied mathematics or data science is highly recommended.
Expected knowledge: Mechanistic modelling of the transmission of pathogens, Bayesian inference models, and command of scientific programming languages such as Python and C++ would be highly recommended.
Appreciated skills: Experience in phylodynamic modelling, knowledge in machine learning, and ability to process complex, heterogeneous datasets would be appreciated.
Skills: Writing and submission of scientific papers in international peer-reviewed journals, presentation of work at conferences, supervision of doctoral students and young researchers, and involvement in collaborative, cross-disciplinary and international projects.
Candidates should have a good command of English, 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).

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. 

How to apply

1. I download the applicant guide Guide for applicants 2026 pdf - 1.41 MB
2. I write down the profile number CR26-SA-1
3. I apply GO