We are a Marie Skłodowska-Curie Innovative Training Network (ITN) project funded by the European Commission, under their H2020 programme. The network consists of 10 Universities and three partner organisations across Europe, and brings together scientists from Astrophysics, Computer Science, Ecology, Geomorphology, Hydrology, Neuroscience, Systems Biology and Social Science.
i-CONN is training a new cohort of researchers specialized in the developing field of Connectivity Science who will be capable of developing interdisciplinary approaches to connectivity across a range of disciplines and real-life applications in the next five to 10 years.
Connected across the continent
i-CONN is training 15 young Early Stage Researchers (ESRs) to become experts with a unique skill set that includes interdisciplinary scientific techniques, through bespoke training courses and through a series of secondments to partner institutions across the EU.
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United Kingdom
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United Kingdom
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United Kingdom
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France
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Germany
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Czech Republic
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Netherlands
Research Themes
The research projects are aligned with the work packages and broadly fall into 5 key themes:
Network graphs are a key tool to analyse interactions between a set of entities. i-CONN projects will investigate structural connectivity/functional connectivity relationships on graphs with a focus on minimal models and self-organized collective patterns.
Network structure depends partly on the scale at which the fundamental unit of the network is represented. i-CONN projects will investigate how the representation of the fundamental unit impacts our understanding of system dynamics at larger spatial scales, and will explore how the structure and properties of networks can be used to determine how shifts in network topology can result in novel systems.
Central to i-CONN is establishing a set of common methods that can be used to investigate connectivity-related research questions across wide-ranging disciplines. i-CONN projects will delve into classic and powerful techniques related to complex systems, including graph theory, probability theory and statistics, as well as modern and promising ones such as Network Science, machine learning and data mining techniques and tools.
Critical nodes are widely considered to be a set of nodes whose deletion results in maximum fragmentation of the network. i-CONN projects will explore how critical nodes within a range of different types of complex systems become key processing points in space and time that shape system evolution and how they might be manipulated to alter system dynamics.
Changes to structural and functional connectivity within a system can have affect the resilience of different system components. I-CONN projects will explore how network measures can be used to identify changing connectivity properties that impact resilience.
