Scholarship opportunities

PhD Scholarship Opportunities at Geospatial Research Institute Toi Hangarau

Wednesday, September 20, 2017

The GRI has a series of projects available for students wishing to do a PhD. Closing date 8th October. There are some dedicated Geospatial Research Institute Toi Hangarau scholarships and UC doctoral scholarships UC doctoral scholarships available for top students. To apply email your CV, cover letter and research proposal to wayne.tyson@canterbury.ac.nz

Projects are listed below. If interested please contact the academic staff member named.

UAV Swarm Intelligence to identify and track dispersed airborne hazards.
Resource allocation and task management in Fog-enabled IoT networks for smart cities.
Towards a Multi-Hazard Approach for Flood Risk Assessment in New Zealand
Development of a National-Scale Hydrological Modelling Tool for the Assessment of Fresh Water Quality in New Zealand
A Collaborative System for Remote Geospatial Data Sourcing and Multimodal Real-time Data Experience: A technological feasibility study
Spatial variations in Māori health
How do ‘emerging’ geo-spatial technologies add to understanding of health?
Light and bipolar disorder 

UAV Swarm Intelligence to identify and track dispersed airborne hazards.
Research Question: Could a ‘swarm’ of UAVs be used to map out the perimeter, and track, a chemical or similar cloud in the atmosphere? This could be used, for example, to monitor fumes from a chemical spill or toxic fire (the recent incident in Houston comes to mind). It could have application to monitoring the edge of volcanic ash plumes which create a hazard to jet aircraft (eg Iceland 2010). Or, perhaps particulate and NOx concentrations around urban areas (highly topical in Europe), or other pollutants around industrial areas which may be hazardous to adjacent urban and residential areas. Maybe organisations like NIWA could use it to improve weather monitoring and forecasting, with spin-off benefits to farmers and other sectors that rely upon accurate localised weather forecasts. Our approach would use the principles of ‘swarm intelligence’ to control multiple, autonomous but collaborating UAVs. The basic goal would be for the UAV swarm to “peg-out” the perimeter of a volume in the atmosphere by first identifying the extent of the dispersed airborne hazard, and then tracking an ‘isoconcentration’ surface to monitor and follow the extent of the hazard. This requires good communications across the network of UAVs, on-board intelligence, and advances in flight planning and control. We would also need to select or develop appropriate sensor technology for one or more of the potential applications, however we believe the basic comms, flight planning and control problems related to the UAV swarm could be fairly generic for a range of sensors and end-uses.
For more information contact Dr Graeme Woodward graeme.woodward@canterbury.ac.nz

Resource allocation and task management in Fog-enabled IoT networks for smart cities.
The vision of Internet of Things (IoT) technology is to build connections between any two things when necessary. Smart city is among the various use cases of IoT. People can have smart parking, intelligent transportation, and intelligent hospitals in a smart city. The traditional way to implement the idea of smart city is to attach sensors to things and send the generated data to the cloud to get processed. For some application such as intelligent transportation, this might not work due to its requirement on latency. Usually the things are physically far away from the cloud. Moving some resources and tasks to the network edge has been proposed to meet the temporal requirement. This new paradigm is called Fog computing. The IoT network with Fog computing paradigm is the so-called Fog-enabled IoT network.
For more information contact Dr Graeme Woodward graeme.woodward@canterbury.ac.nz

Towards a Multi-Hazard Approach for Flood Risk Assessment in New Zealand
Globally, more than 1 billion people live on floodplains, there is significant estimated global exposure to flood inundation (e.g. estimated at 46 trillion USD by Jongman et al. 2012), and recent research suggests that flood risk is likely to increase for many countries as a result of climate change (e.g. see Alrieri et al. 2017). Flood risk assessments provide planning and emergency management authorities with crucial information which can allow them to implement improved flood mitigation and response measures; they are usually carried out for at a local or regional scale based on statistical analyses and computational modelling which utilise observations for historical flood events, or projections produced by climate models. However, there is an urgent need for multi-hazard risk assessment (e.g. Lui et al. 2016) which can account for the impact of other hazards on flood risk at a variety of spatial and temporal scales. In New Zealand, for example, the February 2011 earthquake in Christchurch led to an increase flood risk for a sizable parts of the city through soil liquefaction. The areas with the highest flood risk were placed in the “red zone”, within which buildings were removed and the floodplain rehabilitated. Reductions in river slopes, through spatial variation in liquefaction, have also increased flood risk in parts of the city; sea-level rise under climate change is likely to further exacerbate the situation. Drawing from Christchurch and New Zealand for case studies, this PhD project will develop a methodological framework for the quantification and assessment of terrestrial flood risk which accounts for the impacts of earthquakes, storm surge, sea-level rise and other natural hazards.
Required attributes for a successful candidate include:
• Experienced with the use of GIS, preferably open-source systems such as QGIS.
• Computer programming skills, or a clear ability and willingness to acquire them; experience with C++/ C# and Python/ R would be an advantage.
• Skilled in geospatial/ temporal data processing and statistical analysis
• Strong communication skills, including a good academic writing and presentation.
• Organised, meticulous attention to detail, self-starting, focussed and an ability to work independently.
For more information contact Professor Matthew Wilson matthew.wilson@canterbury.ac.nz

Development of a National-Scale Hydrological Modelling Tool for the Assessment of Fresh Water Quality in New Zealand
New Zealand has an abundance of freshwater which is generally of high quality, but intensification of agricultural practices have led to an increase in contaminants entering river systems, with generally worsening concentrations of nitrate-nitrogen; in addition, increasing sedimentation and losses of wetland systems presents a risk of extinction to endemic species which threatens New Zealand’s biodiversity (Ministry for the Environment & Stats NZ, 2017). The Government of New Zealand recently published targets for improved water quality, through its initiative Clean Water: 90% of rivers and lakes swimmable by 2040 (Ministry for the Environment, 2017). This PhD project will work towards the development of the geospatial tools and spatial data infrastructure required for the analysis of water systems at a catchment to national scale, allowing for rapid implementation by land resource managers. The system will implement a hydrological model which will enable (i) quantifying water availability and quality; (ii) tracking nutrients and pollutants through aquatic systems; and (iii) assessing the impacts of land management practices or mitigation measures on water quality.
Required attributes for a successful candidate include:
• Experienced with the use of GIS, preferably open-source systems such as QGIS.
• Computer programming skills, or a clear ability and willingness to acquire them; experience with Fortran and Python/ R would be an advantage.
• Skilled in geospatial/ temporal data processing and statistical analysis, particularly of large datasets.
• Strong communication skills, including a good academic writing and presentation.
• Organised, meticulous attention to detail, self-starting, focussed and an ability to work independently.
For more information contact Professor Matthew Wilson matthew.wilson@canterbury.ac.nz

A Collaborative System for Remote Geospatial Data Sourcing and Multimodal Real-time Data Experience: A technological feasibility study
This PhD project explores the potential of current technology through the development and evaluation of a system for remote collaboration in the field of geospatial data collection and multimodal experience. The idea is to enable a remote agent to collect data in the field (e.g., a natural environment) while one or more remote collaborators (audience) have the possibility to interact with the collected data using a purpose-build multimodal sensory system in the lab (e.g., an indoor research lab, government facility, public demonstration hall). This project comprises five research challenges:
1. Data capturing Current technology such 3D LIDAR, high-definition and high framerate cameras, as well as other technologies, are now established enough to allow data capturing of adequate quality. However, it remains to be researched to what extent the data can be fused and understood to uncover more insights.
2. Tracking of the remote agent and data synthesis Constructing and updating data of the remote environment while simultaneously keeping track of an agent's location and orientation is still a challenge within the research environment. Several solutions using hybrid-tracking approaches including more than one sensor (e.g., differential GPS, inertial measurement units, as well as computer vision approaches) will be needed.
3. Wireless data transfer While only a few years ago real-time data sharing of this magnitude would have been impossible, current (research) technology is able to handle the amount of data. Ways to optimize the data-streaming/packaging to keep it synchronized, however, needs to be investigated (e.g., through the integration of smart pre-filtering).
4. Interaction & multimodal feedback The key contribution of this research is to allow people to experience the captured data using more than just their visual sense. The aim is to provide an experience that also provides stimuli for the somatosensory system and engages the user(s) in more immersive ways. This also requires a precise and low-latency tracking of the audience-user's body and movement.
5. Integration & User-evaluation All the systems listed under point 1 to 4 need to be developed and integrated in a coherent system and evaluated with in user-studies.
Project Outline
The project will be carried out in two (potentially parallel) processes. The first step will be working on offline data that has been pre-captured (e.g., coastal hydrogeology data). The requirements for interaction possibilities with these data including real-time exploration, multimodal display and feedback, will be defined and evaluated. In particular, interface-technology that supports navigating the data in a non- or low-fatiguing way will be explored. Possibilities to integrate vibrotactile feedback will be explored.
The optimal way to visualise the data in sync with the stimulation of the other sensory modalities needs to be explored in depth. This includes the degree of allocentric/egocentric perspective. Particularly important is to research how the limbs that are directly involved in the interaction are visualized in the system.
The second step of how to stream and sync the data in real-time will be explored. In particular, an adaptive semi-supervised approach to pre-filter data will be explored. The idea is to optimally allocate transfer-bandwidth between the different objects so that the object of immediate interest gets a representation at the highest fidelity, whereas less important objects will receive less bandwidth. Communication and collaboration possibilities with the remote agent who carries out the data-collection will need to be investigated to optimise the collection of immediately relevant data without compromising the overall data sourcing endeavour.
For mor information contact Professor Rob Lindeman gogo@hitlabnz.org

Spatial variations in Māori health 
Māori are over represented in just about every negative health outcomes in New Zealand. Many of the drivers of this are unknown, but what do we find when examining health at finer geographic scales? Previous geospatial research has identified that at fine spatial scales, some areas have health better than expected and others worse. Examination of these areas can help us understand more about the determinants of ill health and positive well-being. This project will examine Māori health at a range of spatial scales to help us identify and understand more about the determinants of Māori health and wellbeing. The student will need to have good quantitative skills and GIS expertise. A student who understands Māori culture and values would be preferred. For more information contact Professor Simon Kingham simon.kingham@canterbury.ac.nz.

How do ‘emerging’ geo-spatial technologies add to understanding of health?
An overarching objective of the proposed research is to combine ‘traditional’ residence based approaches and methods with ‘emerging’ mobile geo-spatial technologies and methods. This will be done using unique datasets you will collect. This will involve collection of smartphone location data to develop and establish a GeoQuantified Community (GQC).
Objective A: Establish the extent to which ‘traditional’ residence based methods are augmented by ‘emerging’ mobile geo-spatial technologies in health geography. There is arguably a paradigm shift in the collection of individual and population data emerging within the health sector. It is now possible to have continuous, real-time location information on individuals (‘quantified self’) and on groups of people (‘quantified community’). You will establish the degree to which this ‘emerging’ geo-spatial information is useful, in the sense that it adds additional understanding beyond the existing knowledge of place effects on health (GeoHealth). The outcome will be a better understanding of place effects on patients as well as the degree to which exposure may change as patients move around changing environments day by day.
Objective B: Develop an exposome model using location data. Develop models of environmental and location based exposure surfaces (e.g. exposure to greenspace or alcohol outlets) to link with personal location data (GQC). You will also draw on literature in the area of mhealth and health geography. The student will need to have good quantitative and GIS/geospatial skills. Some knowledge of mobile applications would be an advantage.
For more information contact Dr Malcolm Campbell malcolm.campbell@canterbury.ac.nz.

Light and bipolar disorder
Circadian rhythms are vital for human health. They are entrained by a combination of natural signals, predominantly light, and social signals “zeitgebers” – for example regular social interactions or work. Disruption of social signals may occur when for example a person experiences a traumatic life event such as a bereavement or a job loss. Disruption of biological signals may occur during a change of season – as the entraining effect of light, for example reduces – or it may occur following transmeridian travel. The relative contribution of biological compared with social signals probably varies between individuals.
Bipolar disorder appears to be a condition in which not only are circadian rhythms disrupted, but further disruption frequently results in relapse into an episode of mood disturbance. Abnormal sleep and circadian rhythms have been found in people with bipolar disorder when they are euthymic and when they are unwell and these abnormalities have also been shown in people at high risk of developing bipolar disorder. Disruption of these rhythms is also associated with longer periods of unwellness.
For mor information contact Dr. Philip Schluter philip.schluter@canterbury.ac.nz

For more information on the University of Canterbury Doctoral Scholarship go to - http://www.canterbury.ac.nz/scholarshipsearch/ScholarshipDetails.aspx?ScholarshipID=6935.127

The closing date for these scholarships is now 8th October 2017