Connectionists: Post-doctoral position (18 to 24 months) at INRAE - UMR TETIS, Montpellier, France

Tue May 25 10:54:36 EDT 2021

*Topic *

Spatio-Temporal analysis of epidemiological events on complex networks
issued from large volumes of heterogeneous data

*Keywords  *

Complex networks, network analysis, multi-scale, spatio-temporal
resolution, heterogeneous data

*Context  *

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.

*Methodology*

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.

*Gross Salary *

2300 to 2900 based on previous professional experience.

*Candidate profile*

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.

*Application instructions:*

Qualified applicants are invited to send their application to Maguelonne
Teisseire (maguelonne.teisseire at inrae.fr) and Roberto Interdonato (
roberto.interdonato at cirad.fr) as a single pdf file containing a cover
letter describing their research background and motivation, a detailed CV
and the contact details of up to three referees.

*Application deadline:* June 28, 2021

*Interviews for selected candidates* : July 2, 2021

*Bibliography*

R. Adderley, P. Seidler, A. Badii, M. Tiemann, F. Neri, M. Raffaelli. Semantic
Mining and Analysis of Heterogeneous Data for Novel Intelligence Insights.
Proc. of The Fourth International Conference on Advances in Information
Mining and Management, IARIA, p.36-40, 2014

Goel R., Sallaberry A., Fadloun S., Roche M., Valentin S., Poncelet P.
EpidNews: An epidemiological news explorer for monitoring animal diseases.
In : Proceedings of the 11th International Symposium on Visual Information
Communication and Interaction (VINCI 2018), Växjö, Suède, Août 2018.

P. Cimiano, L. Schmidt-Thieme, A. Pivk, S. Staab. Buchtitel: Learning
Taxonomic Relations from Heterogeneous Evidence. Proc. of the ECAI 2004
Ontology Learning and Population Workshop, 2004

A. Henriksson, J. Zhao, H. Boström, H. Dalianis. Modeling Heterogeneous
Clinical Sequence Data in Semantic Space for Adverse Drug Event Detection.
Proc. of IEEE Int. Conf. on Data Science and Adv. Analytics, 2015

Roberto Interdonato, Raffaele Gaetano, Danny Lo Seen, Mathieu Roche,
Giuseppe Scarpa:

Extracting multilayer networks from Sentinel-2 satellite image time
series. Netw.
Sci. 8(S1): S26-S42 (2020)

Roberto Interdonato, Matteo Magnani, Diego Perna, Andrea Tagarelli, Davide
Vega:

Multilayer network simplification: Approaches, models and methods. Comput.
Sci. Rev. 36: 100246 (2020)
Roberto Interdonato, Martin Atzmueller, Sabrina Gaito, Rushed Kanawati,
Christine Largeron, Alessandra Sala: Feature-rich networks: going beyond
complex network topologies. Appl. Netw. Sci. 4(1): 4:1-4:13 (2019)
Matteo Magnani, Obaida Hanteer, Roberto Interdonato, Luca Rossi, and Andrea
Tagarelli. Commu-

nity detection in multiplex networks. CoRR, abs/1910.07646, 2021. *(to
appear on ACM Computing Surveys)*
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