Research Associate: CHANCE

The Alan Turing Institute

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.

You can find out more about the CHANCE project at: https://www.turing.ac.uk/research_projects/chance-coupled-human-natural-critical-ecosystems/

Person Specification

Essential:

  • A PhD (or equivalent experience and/or qualifications) in network science, statistical physics, control engineering, and/or applied mathematics
  • High-quality publication record, clearly demonstrating the originality and rigour of the scientific work of the applicant.
  • Data wrangling and programming experience in a network analysis using Python, R, or C/C++.
  • Excellent written and verbal communication skills including the ability to present complex or technical information.

Desirable:

  • A publication track record in complex systems and/or network science journals.
  • Good fundamental understanding of network science, stability, and complex systems modeling.
  • Experience in network visualization and GIS visualization.
  • Experience with working with people from different disciplines.

For further information about the role, and to download the full Job Description please Click Here.

Application Procedure

If you are interested in this opportunity, please send your CV, with contact details for your referees and a covering letter to jobs@turing.ac.uk. If you have questions or would like to discuss the role further with a member of the Institute’s HR Team, please contact them on 0203 862 3375 or email HR@turing.ac.uk. Informal inquiries are welcomed, please contact Dr. Weisi Guo: wguo@turing.ac.uk

This is an exciting opportunity to join a diverse, growing organisation with a strong team of individuals at the helm. There is a generous benefits package including Health Care, enrolment into a generous pension scheme, Cycle2work and childcare vouchers. The Institute also offers 30 days’ holiday per year exclusive of bank holidays.

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