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The goal of the CHANCE (Coupled Human and Natural Critical Ecosystems) project is to develop fundamentally new data science approaches to modeling large-scale networks. Leading researchers that are making advances in complex networks, graph theory, and control theory are welcomed to apply.

Recent advances in network science have enabled researchers to understand the resilience of large-scale topological systems and develop algorithms to improve their robustness in presence of different classes of failures and perturbations. The CHANCE project will enhance our understanding of large-scale complex systems and create a platform to translate fundamental research into real-world impact.

The 3-year project is funded by the Lloyd’s Register Foundation (LRF) and part of the Program for Data-Centric Engineering (DCE). As concrete case studies, the project will focus on coupled critical infrastructures (CI) that span both urban cores and rural peripheries. A targeted outcome of the project will be to create a data-driven modeling framework that can support and inform stakeholders in many different ways including (i) quantifying and understanding the stability of critical systems (ii) prioritizing resilient investments, (iii) developing resilient adaptive algorithms for cyber-physical systems, and (iv) educating and informing the public about risk, uncertainty, and resilience.

These exciting collaborations between network science and engineering will take place at the Turing and in conjunction with the partner universities of University of Warwick, UCL, and Imperial College. At the Turing, access to both a large number of research experts, useful data sets on CI networks, and cloud computer facilities is available.

The Research Associate (RA) will lead the development of network science inspired control algorithms for the CHANCE project. The RA will work closely with investigators and other RAs at the partner universities to validate new models for network resilience. The CHANCE project will work closely with a number of industrial partners to create representative network science models.

The RA will also work with others to perform performance evaluation and comparison with other approaches, and in the preparation of conference and journal papers reporting on the results; to contribute to the ideas in the ongoing project, including looking at potential longer term impact development; and to plan the integration of any new ideas with emerging grant proposals. The position is suitable to candidates who have completed a PhD degree in a relevant field such as: data science, network science, networked systems, control engineering, and/or applied mathematics.

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