The post-holder will be based at the Department of Architecture, but will liaise and collaborate with their equivalents in UCB and NUS. The core aim of these roles is to develop research strategies with the purpose of identifying and proposing new research projects.
The main deliverables for the overall research initiative are: 1) The production of contextual datasets assembled and documented through field work and crowdsourcing.
2) The specification of conceptual Smart Design processes where predictive feedback informs urban design and planning.
3) The development of prototypes of Smart Design systems and human-centric predictive models.
4) A set of case studies for both defining and demonstrating Smart Design in practical use.
The Cambridge team will focus on the development of evaluative models. The aim is to develop and incorporate human-centric parameters, such as health and social equity, into an urban design analysis framework. The team will first develop analytics that can interpret newly-available person level digital traces from administrative and operational databases and voluntarily contributed sensing data from citizens. Based on this data, the team will then develop agent-based models that aim to give predictive feedback on how citizens adapt their living and working to changes in the built environment. These preliminary evaluative models will be integrated into the systems being developed by UCB and NUS. A case study will demonstrate how these systems and models can be used to assess the impacts that urban design interventions and regulations have on human-centric parameters. UCAM will focus on the impacts on walkability and human comfort, with a focus on the impacts on low income and underprivileged citizens.
In summary, the project will provide a proof-of-concept for a systematic and rigorous way of linking urban design decisions with human-centric parameters, thereby demonstrating the potential to improve quality of life.
Fixed-term: The funds for this post are available for 12 months in the first instance.
To apply online for this vacancy, please click on the ‘Apply’ button below. This will route you to the University’s Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.
For any further information, please contact Beau Brady: firstname.lastname@example.org
Please quote reference GC10946 on your application and in any correspondence about this vacancy.
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