Postdoctoral position: Epidemiological Modelling, Bayesian Inference and Artificial Intelligence

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INRAE presentation

The French National Research Institute for Agriculture, Food, and Environment (INRAE) is a major player in research and innovation. It is a community of 12,000 people with 272 research, experimental research, and support units located in 18 regional centres throughout France. Internationally, INRAE is among the top research organisations in the agricultural and food sciences, plant and animal sciences, as well as in ecology and environmental science. It is the world’s leading research organisation specialising in agriculture, food and the environment. INRAE’s goal is to be a key player in the transitions necessary to address major global challenges. Faced with a growing world population, climate change, resource scarcity, and declining biodiversity, the Institute has a major role to play in building solutions and supporting the necessary acceleration of agricultural, food and environmental transitions.

Work environment, missions and activities

You will join the Epidemiology of animal and zoonotic diseases joint research unit, which is recognised for its research combining field studies, mechanistic modelling, spatial analyses and genomic approaches. You will be based on the VetAgro Sup campus in Marcy-l’Étoile, near Lyon, and will work primarily with Francesco PINOTTI, Xavier BAILLY, Guillaume FOURNIÉ and Julien THÉZÉ. You will also collaborate with several French and European partners through the two projects to which this position is linked:

• The Horizon Europe project GeoAI4EI (GeoAI Toolbox for Epidemic Intelligence) aims to develop a new generation of artificial intelligence and modelling tools to strengthen preparedness and response to health emergencies across Europe. The project brings together leading partners in artificial intelligence, spatial analysis, epidemiological surveillance and public health. 
• The French National Research Agency (ANR) project CRISPOX aims to develop models of lumpy skin disease virus transmission in cattle in France and calibrate them using innovative inference methods, in order to evaluate different surveillance and control strategies. 

Your main responsibility will be to develop a new generation of simulation-based inference methods, harnessing recent advances in artificial intelligence to calibrate spatially explicit mechanistic models of pathogen spread using multiple data sources. The approaches developed should also enable parameter estimates and projections to be updated in near real time as new data become available. 

More specifically, you will contribute to the development of methods based on artificial intelligence algorithms – including Neural Posterior Estimation (NPE), Neural Likelihood Estimation (NLE) and related approaches – allowing the simultaneous integration of heterogeneous data sources: epidemiological data such as incidence or seroprevalence, genomic data and outputs from phylodynamic analyses, host demographic and mobility data, environmental and geospatial data, and information on public or animal health interventions.

You will apply the methods developed to different epidemiological models simulating the transmission of pathogens of relevance to human and animal health, including seasonal influenza and SARS-CoV-2 in humans, as well as lumpy skin disease virus in cattle. 

The overall objective of this work is to develop tools capable of rapidly estimating key epidemiological parameters, rigorously quantifying the uncertainty associated with these estimates, and providing regularly updated projections to inform preparedness, surveillance and response to health emergencies.

Finally, you will implement the methods developed as reproducible analysis pipelines in Python and/or R, and contribute to their documentation and dissemination as open-source tools for the scientific community. You will also contribute to the dissemination of the research through scientific publications and presentations at consortium meetings and international conferences. 

Specific working conditions: The position involves regular interactions with French and European partners in the GeoAI4EI and CRISPOX consortia. Occasional travel within France and Europe will be required to attend project meetings, methodological workshops and scientific conferences. Partial remote working is possible in accordance with the Institute’s regulations.

Training and skills

Recommended qualifications: PhD in epidemiology, ecology, statistics, artificial intelligence, physics, applied mathematics or a related quantitative discipline.

Desired expertise: Strong knowledge in at least two of the following areas: mechanistic modelling, Bayesian and/or simulation-based inference, deep learning, phylodynamic modelling, and genomic analyses. Good proficiency in Python and/or R is expected, together with experience using several of the following tools or environments: PyTorch, JAX, Pyro, sbi, Stan, Git and Linux.

Desirable experience: Experience in modelling pathogen transmission dynamics in structured human or animal populations. Experience integrating complex datasets for model calibration and developing reproducible analysis pipelines would also be an asset.

Skills: Ability to design and conduct research with a high degree of autonomy, ability to work effectively within interdisciplinary and international teams, ability to disseminate research through publications in international scientific journals; excellent spoken and written English. 

INRAE's life quality

By joining our teams, you benefit from (depending on the type of contract and its duration):

- up to 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.

How to apply

I send my CV and my motivation letter