Research Associate

The Alan Turing Institute

The goal of the CHANCE (Coupled Human and Natural Critical Ecosystems) project is to establish a new network science based modeling framework for critical infrastructure systems. The 3 year project will focus mainly on coupled water critical infrastructures (CI) that span both urban cores and rural peripheries. Further consideration to the wider food, transport, and energy nexus is also part of the project. The idea is to obtain new network science knowledge to complement existing agent-based scenario simulation methods. We will seek to understand the general stability of these ecosystems, as well as their capacity for adaptation in the face of attacks and perturbations. A concrete outcome of the project will be to create a big data CI modeling platform that can inform stakeholders of: (i) the stability and resilience scaling laws, (ii) prioritizing resilient investments, (iii) develop resilient adaptive algorithms for cyber-physical systems, and (iv) educate and inform the public about risk, uncertainty, and resilience.

The project will be reinforced by a number of associated parallel research projects in water networks, water infrastructure health monitoring, and network science. These will take place at the Turing and in 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, and cloud computer facilities is available.

The Research Associate (RA) will lead the implementation/development of network science algorithms and systems code for the CHANCE project. In the first instance, the CHANCE project will work closely with a number of water distribution and water-related CI operators to create representative network science models. The RA will work closely with investigators and other water engineering RAs at the partner universities to validate new models and develop resilience bounds and rewiring strategies that are both of scientific value and useful from a practical perspective. The RA will 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: network science, statistical physics, control engineering, and/or applied mathematics.

You can find out more about the CHANCE project at:

If you are interested in this opportunity, please head to for a the full outline of the roles and application procedure.

The Alan Turing Institute is committed to creating an environment where diversity is valued and everyone is treated fairly.  In accordance with the Equality Act, we welcome applications from anyone who meets the specific criteria of the post regardless of age, disability, ethnicity, gender reassignment, marital status, pregnancy, religion or belief or sexual orientation. Reasonable adjustments to the interview process can also be made for any candidates with a disability.

Please note all offers of employment a subject to successful security screening and continuous eligibility to work in the UK.

Full details on the pre-employment screening process can be requested from

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