Online Social Networks are one of the main sources of Big Data to analyse the
human social behaviour, and design smart human-centric services that exploit
this knowledge. The post-doc activities will be focused on
(i) collecting and analysing large-scale datasets that describe
the structure of human social networks in OSNs, and
(ii) designing radically new data-centric services based on this knowledge.
Successful candidates will be supervised by Dr. Andrea Passarella
(http://cnd.iit.cnr.it/andrea), and will work in the H2020 SoBigData European
Project, the only EC-funded H2020 Research Infrastructure for the analysis of
human social behaviour from BigData (http://www.sobigdata.eu/).
The post-doc activities will involve developing interdisciplinary approaches,
mixing efficient data crawling and collection techniques, large-scale data
analysis, complex network analysis and modelling, knowledge extraction according
to quantitative models describing the humans’ social behaviour, design of
data-centric services in OSN platforms.
Ideal candidates should have or about to obtain a PhD in Computer Science,
Computer Engineering, Physics, Statistics, or closely related disciplines, and a
proven track record of publications in relevant top-tier conferences and
journals. Preferably, the topic of the PhD should have been in one of the
relevant research areas (BigData analytics, OSN analysis/programming, Complex
network analysis). Good written and spoken communication skills in English are