We are a Marie Skłodowska-Curie Innovative Training Network (ITN) project funded by the European Commission, under their H2020 program. 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.

Eligibility & Applications
Project Information
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Contact

The goal of i-CONN is to train 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.

i-CONN will recruit and train 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.

15 ESR Positions Available

More about i-CONN

In recent years, parallel developments in disciplines as disparate as Ecology, Geomorphology, Neuroscience, Social Science and Systems Biology have focused on what is termed connectivity. In its simplest form, connectivity is a description of the level of connectedness within a system, and can be quantified in terms of structural connectivity (SC) which describes how elements within a system are physically or spatially connected, and functional connectivity (FC) which describes how the strength/presence of these connections varies over space and time.

In all of these disciplines, connectivity has been a transformative concept in understanding and describing what are considered to be complex systems, allowing unprecedented analysis of how such systems behave. Connectivity research is more than a way of grouping elements in a system together based on their SC, but is driven by the notion that a structural network will systematically shape the dynamical processes (and hence the function) within this system. As a consequence, relationships between structural and functional connectivities need to be evaluated and studied on all topological scales. Whilst conceptualisations and approaches to quantify connectivity have evolved largely within their disciplinary boundaries, similarities in the concept and its application among disciplines are also evident.

i-CONN will exploit synergies among different conceptualisations and applications of connectivity. For example, we will evaluate statistical approaches and mathematical theories that have arisen across a range of disciplines in order that we might develop generic connectivity tools to understand better the characteristics of complex systems. i-CONN will provide interdisciplinary training integrating knowledge and methods from different disciplines and stakeholders from the public, private and NGO sectors, using a synthesis of approaches that will lead to transdisciplinarity, whereby a unity of intellectual frameworks will be created beyond the disciplinary perspectives.

i-CONN brings together leading academic and non-academic partners across Europe from those disciplines that have led advances in Connectivity Science with the goal of training a new generation of experts in the application of connectivity concepts to advance both research and practical understanding to address this need. i-CONN will train ESRs to become experts with a unique skill set that includes interdisciplinary scientific techniques and applications of Connectivity Science, to address real-world challenges through a bespoke series of specialized training courses and secondments.

Eligibility & Applications

To satisfy the eligibility requirements set for an Early Stage Researcher funded by Marie Skłodowska-Curie and you must be eligible to be appointed as an Early Stage Researcher:

  1. Should have — at the date of recruitment — less than 4 years of a research career, and not have a doctoral degree. The 4 years are measured from the date when they obtained the degree which would formally entitle them to embark on a PhD, either in the country where the degree was obtained or in the country where the PhD is provided.
  2. Trans-national mobility: The applicant — at the date of recruitment— should not have resided in the country where the research training takes place for more than 12 months in the 3 years immediately prior to recruitment, and not have carried out their main activity (work ,studies, etc.) in that country. For refugees under the Geneva Convention (1951 Refugee Convention and the 1967 Protocol), the refugee procedure (i.e. before refugee status is conferred) will not be counted as ‘period of residence/activity in the country of the beneficiary’.
  3. Satisfy the eligibility requirements to enrol on a PhD degree. This includes acceptable English language requirements if English is not your first language.

All applications are to be submitted via the hosting institution (links to apply will soon be posted). The deadline for applications is 30th January 2020. Applications must include the following:

A copy of your CV
Degree transcripts
A motivation letter
Names of 2 referees

Please indicate in your motivation letter if you are interested in being considered for any of the other PhD positions in our network (and if you give us permission to share your application with the host of that project).

ESR Project Positions

Lead institution
Durham University

Supervisors

Profile: Laura Turnbull-Lloyd
Email: laura.turnbull@durham.ac.uk

Application link: ESR1 (this link will be active soon)

Country
UK

Outline
Critical nodes are considered an important feature of a system that have a great influence on shaping system processes. In the case of ecogeomorphic processes operating in drylands, recent studies have highlighted the role of critical nodes in shaping structural and functional relationships in relation to land degradation (e.g. Turnbull & Wainwright, doi: 10.1016/j.ecolind.2018.11.039). This project seeks to improve our ability to identify critical nodes in complex systems and transform our understanding of their role in shaping system dynamics with a particular emphasis on land degradation in drylands.

Objectives:

  1. To use multiple methods (high-resolution scanning and network-based approaches) to identify critical nodes within a system;
  2. To analyse how critical nodes within complex systems become key processing points in space and time that shape system evolution (i.e. SC-FC relations);
  3. To determine how critical nodes can be manipulated/managed to alter system structure and function. ESR 1 will fulfil these objectives with a primary focus on catchment systems, and will learn from an interdisciplinary perspective by drawing upon analogous critical nodes in neuroscience to explore their role in system dynamics.

Expected Results:

  1. Improved understanding of underlying mechanisms that drive the location and timing of critical nodes in complex systems and their roles in responding to stimuli and processing information/fluxes of energy or matter;
  2. Advances to the theory of the role of critical nodes in Connectivity Science and refinement of key tools for its application;
  3. System-specific provision of baseline information for improved intervention methods (i.e. catchment and river management; key nodes in functional brain networks processing external and internal stimuli and using this knowledge to guide rehabilitation/mitigation of neurological conditions).

Lead institution
Jacobs University

Supervisors
Profile: Marc-Thorsten Huett
Email: m.huett@jacobs-university.de

Application link: ESR2

Country
Germany

Outline

Objectives:
Relationships between structural and functional connectivity (SC/FC relationships) serve as a cohesive, unifying structure to the ITN. The driving force behind ESR2 is the goal to construct minimal models for many of the relationships between structural and functional connectivity (SC/FC relationships) observed in the diverse application scenarios. A minimal model is a mathematical representation (network + dynamical model) that is capable of displaying the desired behaviour and no simpler system can be envisioned. In addition to network architectures directly provided by the application scenarios, various models of random graphs (e.g., scale-free random graphs, small-world random graphs) will be employed. On the level of dynamics, we will resort to a set of generic models: excitable dynamics, random walks and diffusion, flow dynamics, avalanches on graphs. In a subsequent step, these generic models will be refined in a close dialogue with the application projects, in order to capture the most relevant properties of the dynamical processes within each specific application. In this way the project will contribute to a unified scientific framework that captures Connectivity Science and relates the theoretical structures and their properties to specific functions, methods and tools, which have been developed for diverse investigations across the various disciplines involved in the training network.  By doing so, this project will provide a roadmap linking theory and methods as these support the diverse applications.

Expected Results:
(1) A minimal model to each application project capable of qualitatively reproducing the SC/FC correlations observed in the respective application nature, the transferability and the range of validity of each of these SC/FC correlations.  The minimal models will be accompanied with a catalogue of structures, properties, functions, methods and tools related to connectivity. This universal framework will reveal those important theoretical structures, properties and functionalities related to connectivity. Under the light of these universal and strong theoretical results, important problems related to connectivity in various sciences and applications can now be re-investigated with better possibilities of success.

Lead institution
Jacobs University

Supervisors
Profile: Marc-Thorsten Huett
Email: m.huett@jacobs-university.de

Application link: ESR3

Country
Germany

Outline

Objectives:
 Over the last few years, diverse studies have established that many forms of dynamics self-organize on networks to give rise to large-scale collective patterns. The dynamical pattern is then a consequence of the parameters defining the dynamics as well as the network architecture. Examples include Turing patterns on graphs arising from reaction-diffusion systems, self-organized waves around hubs arising in excitable dynamics and synchronization of modules arising in coupled oscillators. In some cases, changing a parameter of the dynamics can trigger a transition from one pattern to another.  These self-organized, collective behaviours are in the focus of ESR3. The work will involve studying such behaviours in model simulations and searching for evidence of such behaviours in the data available within the ITN for the diverse application projects in two example case studies.

Example 1:
To use anatomical connectivity to provide the spatial measure of propagation of activity from key nodes in a neuronal network in healthy (stimulus processing) and pathological scenario (epilepsy, schizophrenia) or severely impaired conditions (blind and paraplegic subjects); the analysis will use anatomical connectivity in a similar way the inclusion of air travel connectivity between cities helped to better understand the (wave-like propagation of) disease spreading. Here, MEG data will be provided by AI (AAISCS) and will relate to training provided in Advanced Courses 2 and 5.

Example 2:
To explore waves of sediment movement through river networks. This is affected by the connectivity between the channel and hillslope sediment sources, and also the downstream connectivity between different channel types (e.g. alternating alluvial and bedrock sections). For a given arrangement of channel reaches, how does the downstream sediment movement change as a function of the rate and type of sediment supply (continuous vs. episodic)? Here, spatio-temporal laser scanning datasets of river channels will be used and will be provided by RAH (UDur) and will link to those used in Advanced Course 5.

Expected Results:
The project will provide a thorough theoretical understanding of self-organized collective patterns on graphs and facilitate the use of this knowledge in the various application scenarios. The results from these projects will not only provide a better understanding of the way brain activity propagates in the brain under specific task demands, but could also help develop novel learning biomarkers (developed from the knowledge accumulated through complex connectivity analysis of sophisticated data recorded with expensive instrumentation) derived from simple and widely available hardware.

Lead institution
Masaryk University

Supervisors
Profile: Christian Kimmich
Email: kimmich@fss.muni.cz

To apply: ESR4

Country
Czech Republic

Outline

Objectives:
Using secondary data from other research projects or statistical offices:

  1. To apply network-based approaches to multiple systems to explore how shifts in network topology result in novel systems in a comparative perspective;
  2. To determine, via comparative analysis, if there are universal characteristics in network topology valid across multiple cases that can be used to anticipate a transition between states;
  3. To explore catastrophic transitions at multiple scales in different contexts; for example, in river systems, land-use change, transitions between different sleep states, brain activity, transitions in political and energy systems.

Expected Results:

  1. Understanding the commonalities of changes in network topology that
  2. can be used to understand the drivers and dynamics of catastrophic transitions, and
  3. the potential for their reversal.

Lead institution
Aix-Marseille University

Supervisors
Profile: Demian Battaglia (AMU, INS)
Email: demian.battaglia@univ-amu.fr

Country
France

To apply: please email your application to Demian Battaglia.

Outline

Tremendous technological progress in the capacity to record simultaneously from multiple brain regions is making increasingly necessary to adopt a system’s perspective when describing brain function. Distant brain regions interact between them in a dynamic manner even to perform very simple tasks and flexibly changing patterns of interactions can be described as time-changing multiplex directed networks, where different layers describe transient inter-regional oscillatory coherence in multiple frequency bands (or across bands). In the framework of this project we propose to study various datasets (from rodent to non-human primate) gathered during large-scale electrophysiological experiments and, more specifically, to analyse them adopting sophisticated tools from complex network analyses that, initially forged in statistical physics or the science of social networks, have not yet been adapted to be compliant with neural datasets.

We will characterize how spatio-tempo-spectral network patterns correlate or predict behaviour and cognitive performance (e.g. in working memory or perceptual tasks) or how they are altered in specific pathological models (e.g. ALS). Network analyses of multi-scale electrophysiological data will be complemented by the design of connectome-based computational models whose simulated oscillatory coherence networks will be compared with the empirically measured one, to reverse engineer possible physiological mechanisms (e.g. alterations of excitability) that may underpin generalized functional connectivity changes.

Lead institution
Durham University

Supervisors
Profile: Rebecca Hodge
Email: rebecca.hodge@durham.ac.uk

To apply: application link coming soon

Country
UK

Outline

This project considers issues of connectivity in relation to sediment transport in fluvial systems, with a particular focus on networks containing bedrock-alluvial channels. A key aspect of this project will be consideration of what forms the fundamental units of a network, and how this can be considered across different scales. The network units that will be investigated are sediment grains, channel bars, exposed bedrock and river reaches. The key question is to what extent an understanding of the structure of these units is necessary to understand sediment fluxes at larger scales. The project will use both field data and numerical modelling. A range of field techniques (e.g. terrestrial laser scanning, structure-from-motion) will be used to collect nested datasets to quantify the structure of these units within one or more UK or international river networks. Field monitoring will also be undertaken to establish sediment mobility within these units. Numerical modelling of network-scale sediment fluxes with varying levels of process representation will be undertaken to address the identified objectives.

Objectives:

  1. To determine the level of process understanding required at one scale in order to predict behaviour at the larger scale;
  2. to identify whether a (predictive) framework can be developed that incorporates connectivity within and between the different scales;
  3. to assess can simple measurements be used to predict future events, e.g. as predictions of efficacy of neurofeedback or other intervention.

Expected Results:

  1. Identification of key network units at different scales in fluvial systems;
  2. Understanding of the extent to which considering a range of scales aids system prediction;
  3. Development of a connectivity framework that can be used to integrate systems operating at a range of scales;
  4. Biomarkers for predictions for future behavioural changes, e.g. neurofeedback or other intervention efficacy from resting state measurements using existing EEG data at AAISCS collected as part of the Horizon 2020 project SmokeFreeBrain.

Lead institution
European University Cyprus

Supervisors
Profile: Vicky Papadopoulou Lesta
Email: V.Papadopoulou@euc.ac.cy

To apply: ESR7

Country
Cyprus

Outline

The project will delve into classic and powerful techniques related to complex systems, such as 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. The techniques will be explored together with application based specialised knowledge in order to provide a rigorous theoretical and algorithmic framework for the identification of semantics networks applicable to the data from different disciplines, such as Astrophysics, Neuroscience and Archaeology. For the astrophysics application, the project will address problems related to galaxy evolution and cosmology. For the astrophysics application, we wish to classify stages of processing and identifying transitions of regional activations of the brain system, using EEG and MEG data and the slow hemodynamic measures (PET and fMRI). The archaeology work involves investigation of possible relation of the evolution of ancient pottery images to how the visual system analyses information.

Objectives:

  1. To apply Bayesian inferencing and graph-theory based methodologies to facilitate the development of a powerful theoretical framework for the identification of semantics networks applicable to the data from different disciplines;
  2. Development of efficient algorithms for automatically extracting the semantics networks from the data and the domain knowledge and their implementation in a unified software tool;
  3. Application and adaptation of the software tool for the analysis of data in astrophysics, neuroscience and archaeology.

Expected Results:

  1. Understanding the minimal common mathematical structures that can be used to derive identification of semantics networks that transform the data in each discipline into meaningful descriptions;
  2. Translating the knowledge in (1) into software tools;
  3. Apply these tools to the targeted disciplines.

Lead institution
BOKU

Supervisors
Profile: Thomas Hein
Email: thomas.hein@boku.ac.at

Application link: ESR8

Country
Austria

Outline

Objectives:

  1. To analyse the linkage between connectivity (and its properties – functional and structural components as well as feedback loops), resilience and stability in industrialized riverine landscapes (IRL) as socio-ecological systems including co-evolutionary aspects;
  2. To determine the specific effects of river floodplain restoration on the resilience of ecological and social components of IRL and their interactions at different scales;
  3. To explore the role of changing connectivity (hydrological, ecological) of different landscape elements and their configuration on resilience aspects in IRL and how these interact with future drivers of change (climate change, land use change) and different ecosystem service profiles of IRL;
  4. To expand this knowledge to other systems with interlinked system domains. The research will be based on an empirical study (ecological and social), targeted experimental work and a modelling approach and will build on large available datasets and own data collections.

We have the option to analyse the role of connectivity on ecosystem functions like self purification (nutrient retention) or biodiversity (habitat availability). The system of interest will be for example the whole Danube stretch and major floodplain areas. Most of them are impacted in their lateral exchange with the river channel and this impacts ecological connectivity and thus various ecosystem functions and related services. Still most of these floodplain areas are also protected areas and important as site for nutrient regulation. Biodiversity of species is related to the lateral exchange, but also to the riverine conditions along the riverine channel (Funk et al. 2019). Management and restoration scenarios could form the basis for an analysis of how connectivity improvements might affect overall outcomes at different scales (floodplain scale, river section and whole river scale).

Expected Results:

  1. Understanding how changing connectivity properties based on river floodplain restoration measures impact IRL resilience at different scales using connectivity based theories such as the Graph Theory;
  2. How resilience of different system components is related to changes in structural and functional connectivity in IRL;
  3. how the knowledge about the critical role of connectivity for riverine management is transferred to other disciplines;
  4. new model frameworks.

Lead institution
Masaryk University

Supervisors
Profile: Christian Kimmich
Email: kimmich@fss.muni.cz

To apply: ESR9

Country
Czech Republic

Outline

Objectives:

Develop a suite of applicable methods including input-output and social network analysis that are appropriate to

  1. identify critical notes and
  2. analyse determinants of change in structural and functional connectivity;
  3. Apply selected methods to environment al research problems of post- carbon energy transitions, the water-energy-food nexus, or floodplains as social-ecological systems; Input-output data will be provided by EXIOBASE 3 and WIOD
    1. Derive methodological contributions that help to describe and analyse input-output data in connectivity studies
    2. and thereby inform the Datathon and contribute to the Advanced Course 5.

Expected Results:

  1. Application of methods suitable to the analysis of critical nodes;
  2. Identification of critical nodes and determinants for transitions in the empirical applications;
  3. A contribution to method development that helps to identify determinants of transition at critical nodes.

Lead institution
University of Vienna (UNIVIE)

Supervisors
Profile: Ronald Poeppl
Email: ronald.poeppl@univie.ac.at

To apply: please email your application to ronald.poeppl@univie.ac.at

Country
Austria

Outline

This ESR will use and combine different aspects of connectivity and resilience science to identity geomorphic hotspots and hot moments in human-impacted catchment systems, and will derive and test management options.

Objectives:

  1. To identify hotspots and hot moments of hydro-geomorphic connectivity in human-impacted catchment systems by reviewing and testing/combining existing connectivity science and resilience approaches in selected medium-sized agricultural catchments;
  2. To develop a conceptual and methodological framework and tools based on the findings of (1);
  3. To test the this framework/tools in selected catchment systems and to derive general implications for connectivity and resilience science and its application in catchment management (e.g. field measurements incl. tracer experiments; i.e. via collecting new data in the course of the project and using already existing datasets), modelling, connectivity indices.

Expected Results:

  1. Critical review of existing connectivity and resilience approaches (incl. social-ecological approaches;
    secondment 1) and their suitability to identification of hotspots and hot moments in human-impacted catchment systems;
  2. Development of a conceptual and methodological framework and tools that
    1. provide a better understanding of the behaviour of human-impacted catchment systems and
    2. serve as a basis for holistic and adaptive catchment management with
    3. tested applications across selected catchments (secondments 2-4).

Lead institution
AAI Scientific Cultural Services Ltd
(Enrolment in Doctoral degree: European University Cyprus)

Supervisors
Profile: Andreas Ioannides
Email: a.ioannides@aaiscs.com

Application link: ESR11

Country
Cyprus

Outline

The brain structure and function are naturally modelled as networks with cytoarchitectonic areas and deep brain nuclei represented as nodes and the white matter or some measure of functional connectivity as edges in a graph.  This project will add to the increasingly popular network model of the brain the coupling to the global electrical current flow generated by the collective neuronal activity.  This model will be tested using Magnetoencephalography (MEG) and electroencephalography (EEG) data.  The ESR will test two hypotheses:

  1. passive flow of electrical current interacts with the processing based on the network of white matter connections
  2. this interaction has consequences for normal activity in awake state and sleep and possibly in pathology. The transferability of connectivity techniques and ideas will utilise results of other projects where the interaction between global flow and network activity is evident, e.g. ecogeomorphology of dryland and/or fluvial environments.

Objectives:

  1. To model network-based and continuous flows and their interactions so that the influence of each component in specific and possibly diverse applications can be estimated quantitatively and in terms of meaningful visualizations and time-dependent connectivity.
  2. To describe spatiotemporal brain network activity, using MEG and EEG data.
  3. Document similarities between processes in the brain and in other fields and explore how these can be generalized so they can be applied to other disciplines.
  4. Review trends in connectivity-based biomarker development with few channel EEG and related IPR issues and relevance to results of the project.

Expected Results:

  1. Understanding the universality of changes in network topology driven by time-ordered events leading to
  2. unifying the methodology for quantitatively describing network and global properties, dynamics and transitions generated by internal and external influences of finite duration and
  3. through this analysis showing how the relevant parts of a network are “seen” from the point of view of one of the components of the system (node or edge), or
  4. from the point of view of transferability of concepts and approaches between apparently unrelated disciplines sharing some common graph theoretical description.
  5. The ESR will prepare a report and a training material for other ESRs on current efforts of developing connectivity based biomarkers with emphasis on the use of few channel EEG and related IPR issues.

Lead institution
Modul University Vienna

Supervisors

Profile: Christian Kerschner
Email: christian.kerschner@gmail.com

To apply: application link coming soon

Country
Austria

Outline

Objectives: 

Using secondary data e.g. World Bank or International Energy Agency (IEA):

  1. Bring in and advance theoretical and empirical insights from Ecological Economics, Industrial Ecology and Energy Analysis;
  2. Use these insights to draw analytical maps of the global network of oil flows in the socio-economic system, taking into account both quantity and quality of the resource including price mechanisms;
  3. Quantification of flows for the design of a simple oil flow-model e.g. system dynamics combined with Input-Output models;
  4. Identify critical nodes that determine flows and that could be used as flow-limiting agents for policies designed to meet carbon budgets and counteract limitations (resource peaks).

Expected Results:

  1. Identification of critical nodes for policy making to secure a smooth transition towards a post-carbon society;
  2. Theoretical insights into the specific functioning of the energy-economy nexus;
  3. A simply quantified model of oil flows;
  4. Making the link between the energy-economy-society nexus and other nexus e.g. the water-economy nexus;
  5. Key insights into the “why” of connections within networks (as to the “how”).

Secondments will consolidate the ESR’s understanding of connectivity theory (with JU) and Ecological and Biological systems theory (IIASA), in order to explore its applicability to the network of critical energy resource flows in the global socio-economic system.  Training in graph and game theory from EUC will enable the selection of appropriate methods to draw an analytical map of that network and the nature and behaviour of its nodes and build a minimal global oil-flow model.

Lead institution
Durham University

Supervisors
Profile: John Wainwright
Email:  john.wainwright@durham.ac.uk

To apply: application link coming soon

Country
UK

Outline

As human populations have settled landscapes in the past, they have changed the vegetation and surface characteristics in ways that have often led to a change in the stability of those landscapes (Wainwright, 2015).  There is thus a paradox to be resolved in that landscape settlement usually produces a landscape that is less resilient and thus less able to support settlement.  This project will investigate this paradox, and look at different ways in which past populations have overcome it, in order to suggest ways in which environmental management of future landscapes might best be carried out, for example as people move as a result of climate change.

Objectives:

  1. To evaluate the ways in which changing types of settlement affect environmental resilience by the application agent-based models in coupled social-environmental systems;
  2. To use multi-level social network analysis as well as social-environment network analysis to evaluate the resilience of emergent social and social-environment structures in various human settlements of new environments;
  3. To look at different mappings of and understandings of environment-culture interactions.

Expected Results:

  1. Understanding how self-organization in coupled social-environmental systems changes landscape resilience as a consequence of changing connectivity;
  2. providing a broader understanding of how population resilience can be interpreted;
  3. understanding how environmental change as a result of ongoing population flows (e.g. refugee migration) might be managed;
  4. suggestions of techniques for managing environmental resilience under external forcing;
  5. transfer of network modelling ideas across social science and neuroscience in both directions. The project will use simulated data generated from the modelling and compare them with spatial settlement and population data from existing datasets.

Lead institution
To Be Confirmed

Supervisors
Profile: Christina Prell
Email: c.l.prell@rug.nl

Profile: Louise Bracken
Email: l.j.bracken@durham.ac.uk

To apply: this project is not yet open for applications. However please get in touch via email if you are interested in this project.

Country
To Be Confirmed

Outline

Objectives:

  1. To map the ways in which social relationships and knowledge production are developed and co-evolve during i-CONN among team members;
  2. Identify different types of network features and dynamics within the network of researchers from analysis of empirical data;
  3. Use analysis of empirical data to inform training of the i-CONN project team;
  4. Examine a series of real-world applications, that include physical and social networks in conjunction with partners, which could benefit from understanding multilayering between social and physical networks and mechanisms and processes for knowledge production. Secondary data from partners will be used and supplemented if necessary by empirical data collection;
  5. Reflect on the contribution that i-CONN makes to Connectivity Science.

Expected Results:

  1. Data and analysis of social relationships and knowledge production in i-CONN;
  2. A detailed understanding of social network dynamics and networks of knowledge production and how these could be better utilised in real work situations such as catchment management;
  3. An understanding of multi-layering between social and physical networks and knowledge production

Lead institution
To Be Confirmed

Supervisors
Profile: Louise Bracken
Email: l.j.bracken@durham.ac.uk

To apply: this project is not yet open for applications. However please get in touch via email if you are interested in this project.

Country
To Be Confirmed

Outline

Objectives:

  1. Develop a new evidence-base of sources, pathways and transmission rates for unwanted diffuse chemicals in the water-supply chain;
  2. Using minimal models and identify critical nodes to explore the pathways of diffuse chemicals;
  3. Based on empirical data (O1) and modelling (O2), determine the most effective interventions that will prevent pollutants from getting into and being transported through the water network. The above approach will subsequently be used to explore source-pathway-interceptors of pharmaceuticals in water courses.

Datasets used in this project are Environment Agency Open Source Data of diffuse chemicals and pharmaceuticals obtained by routine monitoring operations to comply with the wastewater treatment framework directive and the Water Framework Directive. Other complementary datasets from UK water companies will be made accessible via EA partners. These secondary datasets will be supplemented with empirical data collection in select catchments.

Expected Results:

  1. Data integration across data organisations and across urban-rural landscapes (to feed into the datathon event – month 19);
  2. Development of existing tools from alternative disciplines for understanding sources, pathways and transmission of diffuse pollutants;
  3. Testing and application of these tools to determine the fate of diffuse chemicals across UK catchments and Austrian catchments, and
  4. better understanding of the fate of pharmaceuticals across these catchments.

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