About the Project
An increase in physical activity in a population delivers health benefits to individuals and reduces the burden on health and social care services1,2. Therefore, designing and planning the environment and public services to promote physical activity, such as walking, running and cycling, should be a priority for policy makers. Cities are complex systems, composed of multiple interacting subsystems and these subsystems are designed, managed and operated as if they were independent of each other.
Agent-Based Modelling (ABM)3, a sub-field of artificial intelligence, is a simulation method in which autonomous agents with a specified set of characteristics interact with each other and their environment according to predefined rules. ABM enables us to study complex systems where human behaviour, environmental interactions can be modelled and emergent4,5. The ABM approach enables us to ask “what-if” questions by modifying the agent’s characteristics, the environment and the rules of interaction; these simulations provide new insights for policy makers. This research will investigate whether ABM can be developed to support planning public services and infrastructure to promote physical activity.
https://www.herc.ac.uk/
https://www.research.manchester.ac.uk/portal/john.ainsworth.html
The Alan Turing Institute – About the studentship
The Alan Turing Institute and The University of Manchester offer a number of places each year to motivated graduate students to complete a fully funded PhD. The Turing doctoral studentship scheme combines the strengths and expertise of world-class universities with the Turing’s unique position as the UK’s national institute for data science and artificial intelligence, to offer an exceptional PhD programme.
Turing students will have access to a wide range of exceptional benefits:
• Spend time in both a university research environment and at The Alan Turing Institute.
• Access to a range of training, events, seminars, reading groups and workshops delivered by leaders in research, government and industry.
• Opportunities to collaborate on real-world projects for societal impact with current and emerging industry partners.
• Expert support and guidance through all stages of the studentship, delivered by supervisors who are Fellows of the Turing or substantively engaged with the Turing.
• Networking opportunities and brilliant minds researching a range of subjects with opportunities to collaborate and join or start interest groups.
• Opportunities to supercharge your research with access to cutting edge resources.
Find out more at turing.ac.uk/PhD https://www.turing.ac.uk/phd-at-turing
Entry Requirements
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.
Funding Notes
Fully funded 3.5 years Studentship to commence in September 2019 under The Alan Turing Institute and The University of Manchester with a generous tax-free stipend of £20,500 per annum, a travel allowance and conference fund. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form.
As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
References
1. Reiner, M., Niermann, C., Jekauc, D. and Woll, A., 2013. Long-term health benefits of physical activity–a systematic review of longitudinal studies. BMC public health, 13(1), p.813.
2. Greater Manchester Combined Authority “Taking Charge of Our Health and Social Care in Greater Manchester” http://www.greatermanchester-ca.gov.uk/download/downloads/id/125/taking_charge_of_our_health_and_social_care_in_greater_manchester.pdf
3. Tracy, M., Cerdá, M. and Keyes, K.M., 2018. Agent-Based Modeling in Public Health: Current Applications and Future Directions. Annual review of public health, 39:77-94
4. Yang, Y., Roux, A.V.D., Auchincloss, A.H., Rodriguez, D.A. and Brown, D.G., 2011. A spatial agent-based model for the simulation of adults' daily walking within a city. American journal of preventive medicine, 40(3), pp.353-361.
5. Hekler, E.B., Michie, S., Pavel, M., Rivera, D.E., Collins, L.M., Jimison, H.B., Garnett, C., Parral, S. and Spruijt-Metz, D., 2016. Advancing models and theories for digital behavior change interventions. American journal of preventive medicine, 51(5), pp.825-832.
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