We are now accepting applications for the 2026 Geospatial Research Institute (GRI) scholarship.
Funds awarded:
NZ$35,650/year, over 3 years full-time. This scholarship consists of:
- One UC PhD scholarship ($32,650 per annum), plus
- A GRI top-up scholarship ($3,000 per annum).
Up to an additional $4,000 per year is available from the GRI for travel or other research related expenses (over three years full-time, or pro-rata for part-time study, subject to support from the senior PhD supervisor and approval of the GRI Director).
Funding period: The scholarship is tenable for the period necessary to complete up to 360 points of enrolment
Scholarships available: One
Closing date for applications: 29 May 2026, 17:00pm NZ Time
*Those that submit late, will not be considered.
For information related to selection of PhD candidates please download this link: https://geospatial.ac.nz/wp-content/uploads/2026/04/05_selection_of_phd_candidates2026.pdf
As part of the selection process, the candidates need to select one of the following geospatial projects:
Water & Ice: Exploring impact of terminal lakes on glacier recession
Aim: This project aims to advance understanding of the processes driving ice loss at lake-terminating glaciers, by combining new and existing geospatial datasets with AI-driven remote sensing and data fusion pipelines.
Summary: Globally, the number of glaciers terminating into freshwater lakes is increasing, meaning iceberg calving is becoming major driver of ice loss. Predicting this process remains challenging due to the complex interactions between water and ice including, ice flow, melting above and below the water, and the persistence of submerged ice ramps. Specifically, does the submerged portion of the glacier terminus influence the rate of ice retreat by affecting terminus buoyancy and ice dynamics? Advances in geospatial analysis techniques and 3D data visualisation provide opportunity to target the long-standing and persistent causality dilemma of whether ice velocity drives iceberg calving or iceberg calving drives velocity? Machine learning and AI-driven remote sensing now offer new tools to resolve this question. The successful candidate will combine existing lake depth and limnological surveys with newly acquired high-resolution SAR data and deep learning methods to draw new insights at an actively receding glacier margin in the Southern Alps of Aotearoa New Zealand.
Skills required: A background in earth system science and/or glaciology, with an interest in geospatial analysis and remote sensing, is an advantage. Previous experience with any programming language, GIS platform, data-driven methods (e.g. ML, CNN’s) and/or photogrammetry techniques an advantage.
Contact: Ass. Prof Heather Purdie heather.purdie@canterbury.ac.nz
Using GeoAI and spatial modelling to advance the 3-30-300 rule’s measurement and assess its impacts on equity
Aim: Examining the 3-30-300 rule’s robustness as a neighbourhood quality indicator by developing scalable measurements and assessing its socio-spatial impacts through factors like housing prices.
Summary: This project examines how access to trees, green space, and urban infrastructure influences environmental quality within and between neighbourhoods using the 3-30-300 rule. This rule proposes every resident should have visibility of at least 3 trees, 30% neighbourhood canopy cover, and green space access within 300 metres. Despite its adoption as a guideline in urban forestry and planning, the 3-30-300 rule’s evidence base remains limited in terms of consensus on measuring its components, scalability, and thresholds for socio-spatial impacts.
The research uses an explicitly spatial approach that combines multi-scale LiDAR-derived tree canopy cover, network-based accessibility, and GeoAI methods to capture experiential aspects of tree exposure, and the extent to which that exposure is equitably distributed. Exploring 3-30-300 rule component measurements, and how they interact with socio-spatial aspects of neighbourhoods, will produce a replicable, scalable approach and help determine its robustness as a neighbourhood quality indicator.
Skills required: Strong GIS or spatial data science background, including programming skills (R/Python); skill and experience working with remote sensing data including imagery and LiDAR data; experience with computer vision or GeoAI is a plus.
Contact: Dr. Lindsey Conrow, lindsey.conrow@canterbury.ac.nz
Geospatial Pathways for an Equitable Clean Energy Transition in Aotearoa: A National Atlas for Just Transition Planning
Aim: Develop a geospatial decision-support framework to identify equitable clean energy pathways, integrating infrastructure, socio-economic vulnerability, and energy system optimisation across Aotearoa.
Summary: This PhD will develop a national geospatial atlas to support an equitable clean energy transition in Aotearoa New Zealand. The research integrates advanced GIS and spatial data analysis with energy system modelling to map socio-economic vulnerability, infrastructure access, and renewable resource potential. It will identify where barriers to clean energy adoption exist, such as limited access to rooftop solar, EVs, efficient heating, and distributed storage, and assess how policy and investment decisions impact different communities. Spatial optimisation will be used to evaluate equitable transition pathways, including targeted interventions and community-based solutions. The project includes stakeholder engagement and co-design to ensure relevance and impact. Outputs include a high-resolution national atlas, policy insights, and a decision-support dashboard for agencies and communities.
Skills required:
- Geospatial analysis and GIS (e.g. QGIS, ArcGIS, Python/R spatial libraries)
- Quantitative data analysis and statistical modelling
- Energy systems knowledge (renewable energy, grid infrastructure, or energy policy)
- Socio-economic or environmental data handling (deprivation indices, census data, land-use datasets)
- Science communication and data visualisation
- Proficiency in coding (Python or similar) (desirable)
- Energy system modelling experience (desirable) (e.g. PyPSA, OSeMOSYS, REMix)
- Community or stakeholder engagement experience (desirable)
- Familiarity with New Zealand’s policy, planning, or indigenous (Māori/Te Tiriti) contexts (desirable)
Contact: Rebecca Peer rebecca.peer@canterbury.ac.nz
The human exposome: advancing geospatial and theoretical understandings of health and environmental interactions over time and space
Aim: The project develops the concept of the geospatial exposome.
Summary: Human health is shaped not by single exposures in isolation, but by the cumulative, dynamic totality of environmental conditions encountered across a lifetime. The exposome, encompassing all environmental influences from conception to death, offers a powerful theoretical framework for understanding this complexity; however, its spatial dimensions remain underdeveloped. Most epidemiological research captures only residential environments at one time point, reducing rich, multi-contextual exposure histories to a single address at a single point in time. This PhD project addresses that theoretical and methodological gap by advancing geospatial conceptualisations of the human exposome across home, school, and workplace settings over the full lifecourse. The project develops the concept of the geospatial exposome: a theoretically grounded and computationally realisable framework for representing how spatially distributed environmental conditions accumulate, interact, and produce health outcomes across time. Central theoretical contributions include formalising exposure trajectory modelling across multiple activity spaces, operationalising concepts of critical periods and sensitive windows within a spatial lifecourse framework, and articulating how place-based disadvantage compounds across domains to generate persistent health inequalities. The project will generate both theoretical advances in spatial epidemiology and high-quality, policy-relevant evidence on how place-based disadvantage accumulates across life stages.
Skills required:
- Background in health sciences, geography, epidemiology, or related field
- Geospatial analysis and GIS (e.g. QGIS, ArcGIS, Python/R spatial libraries)
- Quantitative data analysis and statistical modelling
- Socio-economic, environmental data handling (deprivation indices, census data, land-use datasets)
- Good written and verbal communication skills
- Ability to work with interdisciplinary research teams
- Interest in environmental health and healthcare systems
- Attention to detail and strong analytical thinking
Contact: Dr Matt Hobbs, matt.hobbs@canterbury.ac.nz
Prospective PhD student applications must include the following five items:
Cover letter explaining motivation for doing a PhD outlining interest and experience in geospatial methods and analysis.
Curriculum Vitae including a list of any prior publications.
Contact details of at least two academic or professional referees
A GPA report obtained from https://support.scholaro.com/portal/en/kb/articles/canterbury (those with New Zealand or United States qualifications are not required to use Scholaro).
Please send your completed application materials to:
*Those that submit late, will not be considered.
In recognizing our Tiriti o Waitangi responsibilities and in valuing diversity in our institutions, we look forward to a diverse applicant pool. Māori scholars are especially encouraged to apply and would be connected with Māori academics and support staff at Te Whare Wānaga o Waitaha | University of Canterbury.
