Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : UMR TETIS
Durée : 18 to 24 months
Contact : roberto.interdonato@cirad.fr
Date limite de publication : 2021-07-15
Contexte :
In recent years, the amount of data generated on human and animal health events has increased significantly. Epidemiologists must therefore regularly analyze these data with various spatial and temporal resolutions. The proposed postdoc contract is part of the H2020 MOOD project “Monitoring Outbreak events for Disease surveillance in a data science context” (https://mood-h2020.eu), which brings together 25 partners from 10 countries. This project is led by CIRAD (UMR ASTRE) and aims at improving the detection, monitoring and evaluation of emerging infectious diseases in Europe by using advanced data science techniques on massive multisource data.
The work package 3 “data ingestion and integration” is centered on the linking of heterogeneous data collected and processed in the context of the MOOD project. These data are heterogeneous in terms of domain (e.g., medical, environmental, social) and in terms of format (e.g., textual data, satellite imagery, multivariate quantitative data), and can be originated by both official (e.g., medical institutes, scientific laboratories) and unofficial (e.g., newspapers, social media) sources. By consequence, this diversity is also reflected in the spatial and temporal scales of the data.
More precisely, in the context of this post-doc, we are interested in modeling information about epidemiological events (detected from various data sources that are syntactically and semantically heterogeneous) into complex networks models that can allow advanced spatio-temporal analyses.
Sujet :
The postdoc is focused on the possibility to model the heterogeneous data collected and processed in the context of the MOOD project into advanced complex network models, i.e., networks that integrate spatial and temporal information about the data.
The objective is twofold: (i) to show how heterogeneous data about an epidemiological event can be integrated, aggregated and analyzed into complex network models in order to allow an analysis of the complex spatio-temporal phenomena that characterize the life cycle of an epidemic, and (ii) to define original networks analysis and data science techniques in order fully exploit the information modeled in such spatio-temporal networks.
The research question at the center of this postdoc can be formulated as follows: How can we relate spatio-temporal information from epidemic-related data in order to have a spatio-temporal analysis framework in the One Health context?
More precisely, we wish to propose generic methods to link and aggregate information from heterogeneous sources (in particular official and unofficial sources) into feature-rich networks able to embed spatio-temporal features, that will allow to analyze the life cycle of an epidemic according to its spatial and temporal evolution.
The final aim is then to bring new knowledge to experts, that will represent a precious complement to the classic source of information already exploited in the project. This spatio-temporal linking process will have to take into account some reliability and quality factors associated with the different descriptors, i.e., depending on source types and on the confidence of the algorithms in use.
Profil du candidat :
PhD in computer science.
Preference will be given to highly motivated candidates with research experience in complex network analysis, heterogeneous data science and data science applied to epidemiology related tasks.
Formation et compétences requises :
PhD in computer science.
Preference will be given to highly motivated candidates with research experience in complex network analysis, heterogeneous data science and data science applied to epidemiology related tasks.
Adresse d’emploi :
500 rue Jean François Breton, 34090, Montpellier
Document attaché : 202105251458_PostDoc MOOD 2021.pdf