The University of Exeter and the University of Queensland are seeking exceptional students to join a world-leading, cross-continental research team tackling major challenges facing the world’s population in global sustainability and wellbeing as part of the QUEX Institute. The joint PhD programme provides a fantastic opportunity for the most talented doctoral students to work closely with world-class research groups and benefit from the combined expertise and facilities offered at the two institutions, with a lead supervisor within each university. This prestigious programme provides full tuition fees, stipend, travel funds and research training support grants to the successful applicants. The studentship provides funding for up to 42 months (3.5 years).
Ten generous, fully-funded studentships are available for the best applicants, five offered by the University of Exeter and five by the University of Queensland. This select group will spend at least one year at each University and will graduate with a joint degree from the University of Exeter and the University of Queensland.
Find out more about the PhD studentships http://www.exeter.ac.uk/quex/phds
Successful applicants will have a strong academic background and track record to undertake research projects based in one of the three themes of: Physical Activity and Nutrition; Healthy Ageing; and Environmental Sustainability.
The closing date for applications is midnight on 19 May 2019 (BST), with interviews taking place week commencing 8 July 2019. The start date will be January 2020.
Please note that of the seven Exeter led projects advertised, we expect that up to five studentships will be awarded to Exeter based students.
Exeter Academic Lead: Professor Brett Day, Land Environment Economics and Policy Institute, Business School, Exeter [email protected]
Queensland Academic Lead: Professor Jonathan Rhodes, Environmental Management, School of Earth and Environmental Sciences
This proposed programme of research will seek to examine a number of fundamental methodological questions concerning how we should choose to develop landscapes for natural capital that provide ecosystem services (such as clean water, carbon sequestration, and natural areas) so that they maximise outcomes for society. The research will focus on quantifying the uncertainty inherent in the scientific and economic information used to inform such decisions and develop methodologies to support optimal decision-making under uncertainty.
This is an interdisciplinary project suitable for candidates with strong quantitative skills from either a natural science or economics background who have interests in the environment and wish to develop cutting-edge expertise in environment-economy modelling and decision science.
To help inform natural capital decisions, research groups such as the LEEP institute at the University of Exeter and CEED at the University of Queensland have developed sophisticated integrated models that relate economic behaviour to environmental damage to ecosystem service flow change and on to economic valuation (e.g. Bateman et al. 2013, 2016). While these models detail our best guess of changes over space and time, their predictions come with significant uncertainties arising from prediction errors in the integrated models compounded by uncertainty over future environmental and economic conditions.
This studentship will explore selected examples of those integrated models and develop methods that allow the uncertainties in their predictions to be quantified and characterised. Possible model pathways include; (1) grassland reversion from arable crops impacting on downstream flood risks and damage, (2) woodland creation and forestry and recreation benefit flows, (3) pollinator habitat and agricultural productivity, and (4) coastal wetland conservation and biodiversity outcomes. Those models exhibit features that will demand innovations in uncertainty quantification particularly (a) propagation across coupled models (b) spatial and temporal dependence in the productivity of natural capital assets.
Those same complexities also present a serious challenge in thinking how we might best configure the natural capital assets in our landscapes under uncertainty over the returns from different configurations of those assets. Using the same set of modelling tools applied to case study areas in the UK and Australia, a second major innovation of this studentship will be to harness the latest developments in computing sciences, to solve the complex spatial optimisation problems that might identify efficient configurations of the natural capital landscape. This innovation will advance the supervisory team’s recent work applying modern portfolio theory to natural capital problems (Runting et al, 2018).