Call for Applications - Scientia PhD Scholarship
Published on the 18 Jun 2018
The UNSW Scientia PhD Scholarship Scheme is part of our dedication to harnessing our cutting-edge research to solve complex problems and improve the lives of people in local and global communities.
Scientia scholars will have a strong commitment to making a difference in the world with demonstrated potential for contributing to the social engagement and/or global impact pillars of the UNSW 2025 Strategy.
The Scientia Scheme is targeted in that applicants will apply to a specific research area with an identified supervisory team and application is by nomination.
Work on high quality research projects with the best supervisory teams in world class environments
Stipend $40K a year for four years
Tuition fees covered for the full 4 year period
Personal Development coaching and mentoring will form a critical part of your highly personalised leadership development plan
Career Development up to $10k each year to build your career and support your international research collaborations
Indigenous Researchers at least 5 scholarships will be reserved for Indigenous research candidates
Expression of Interest Deadline is Friday 20 July 2018
https://www.2025.unsw.edu.au/apply
Back to the Future: Climate Impacts during the Last Interglacial
The Last Interglacial (130,000-116,000 years ago) is the most recent ‘super-interglacial’, providing an analogue for future change. With global temperatures warmer than present (1-2˚C) and sea level >6 m higher, the Earth system appears to have passed a ‘tipping point’, but with highly uncertain impacts on Australia. This project will use lake and coastal sedimentary and speleothem sequences to reconstruct Last Interglacial climate and environmental changes on sub-decadal to millennial timescales across Australia. The results will be used to inform on the timing, magnitude and impact of a warmer-than-present world on Australia’s water balance, helping adapt to future climate variability.
The successful candidate will be expected to hold a Bachelor of Science (Honours) distinction or higher-class degree in Earth science, physical geography or a related field. The candidate will have analytical skills (including statistical/mathematical skills) and experience with Quaternary science methods.
Supervisory Team: Zoë Thomas, Chris Turney and Andy Baker
Remittance Economies and Inclusive development in the Asia Pacific
Remittances have become an important sources of income and livelihood for many poorer communities in the Asia Pacific. However, evidence for improvements to life and livelihoods, particularly for women at the local level, is unclear. This research investigates the impacts of remittance economics (eg in Nepal and a Pacific place). It critically analyses the social and environmental implications of payments using socio-cultural analyses to unpack the vagaries and unevenness of remittance economies, at the local level. It will generate evidence to rethink global and national remittance policies around the developing world.
The idea candidate will hold a bachelor degree with Honours (first class) or equivalent.
You will have a background in social science research methods as demonstrated in Honours (or equivalent) research.
Evidence of published research is essential.
Supervisory Team: Wendy Shaw, Tanya Jakimow (School of Arts and Social Sciences) and Krishna Shrestha (School of Arts and Social Sciences)
Fusion of Earth Observation data and 3D city models
Many Earth observation platforms (drones, micro-satellites) have become inexpensive, recording massive amounts of data via thermal, colour, infrared, and radar cameras for different applications, such as urban planning, vegetation dynamics monitoring, and natural hazard monitoring. Living in the age of big remote sensing data, currently we face challenges in managing, processing, and efficiently exploiting these data for socio-economic and environmental applications. This project will develop novel 3D data fusion modelling using voxels for context-based, automated information processing and extraction from large databases of disparate remote sensing imagery to bring new perspectives on phenomenon understanding and prediction.
The candidate must have a background in Geomatics with a strong interest in image analysis, OR a background in computer vision with a strong interest in 3D spatial information. In both cases, the candidate should have good programming skills, competence in 3D modelling and spatial analysis, understanding of machine learning, and fascination for Earth science applications.
Supervisory Team: Sisi Zlatanova (School of Built Environment), Graciela Metternicht and Ben Gorte (School of Built Environment)