Postgraduate

Description: The threat of asteroid impacts on Earth necessitates the detection and tracking of potentially hazardous asteroids to develop effective mitigation strategies. This PhD project aims to explore the combined use of the Vera Rubin Observatory (Legacy Survey of Space and Time – LSST) and the Global Fireball Observatory for identifying asteroids impacting the Earth. The main aim of this PhD project is to advance our understanding of the asteroid population and its potential impact on Earth by using LSST and fireball networks to detect and track potentially hazardous asteroids. The secondary aim to to make use of cutting-edge data from the Australian DREAMS telescope infrared survey, a so far poorly explored parameter space for asteroid characterisation. This PhD project has significant scientific and practical significance. Scientifically, it will advance our understanding of the asteroid population and its potential impact on Earth, and it will contribute to efforts to protect Earth from potential asteroid impacts. Practically, it will develop and test new algorithms and software tools for analysing LSST and DREAMS data, which can be used by astronomers and planetary defence experts to detect and track potentially hazardous asteroids, and to develop and implement mitigation strategies.

Suited to: A person with a background in data science, astronomy, or physics.

Main supervisor: Dr. Hadrien Devillepoix

Description: The objectives of this work are to quantify the extent of potentially habitable environments (PHEs) within the crust of Mars, where a habitable environment can be considered most broadly as one which supports: liquid water of varying plausible chemistries (brines), within a range of temperatures appropriate for terrestrial microorganisms. The existence of habitable conditions will be probed using a subset of impact craters that likely to have formed from the presence of water/ice in the subsurface. A range of satellite datasets will be used by the candidate to constrain the depth to, water concentration, salinity of, and age of the PHE, including: impact crater morphometry, mineralogy, rock and grain-sizes and their distribution. The remote sensing datasets used will include: high resolution camera imagery, elevation, thermal inertia, and multi-and hyperspectral data across the visible, shortwave infrared and thermal wavelengths from a range of sensors and satellites. The candidate will take existing machine learning image fusion algorithms, and further develop them to the unique application of infilling the particular temporal, spectral and spatial gaps of Mars satellite sensors to generate novel satellite data products at enhanced spectral and spatial resolutions than what is currently available. The output of the work will be maps of high priority PHE that occur near-surface, or zones where PHEs cluster and or conditions of habitability are likely to have persisted for a significant period of time.

Suited to: A person with a background in data science or geophysics.

Main supervisor: Dr. Eriita Jones

Description: Objectives: The objectives of the research are to develop a proof-of-concept fault tolerant distributed spacecraft architecture and compare the approach and performance with the traditional approaches (component hardening, component redundancy using voter circuits) via test and analysis for a variety of mission scenarios both in Earth orbit, and beyond. The trade-off between fault tolerance provided by system level redundancy and increased complexity viewed through the lens of mission risk will be a key focus of the work. Whilst this entire project can be successfully completed with ground testing alone, an additional aim is to also conduct some on-orbit testing of the approach.

An early step in the work may focus on developing and testing a proof-of-concept implementation using microcontroller or microprocessor based nodes (representing spacecraft subsystems) connected via an IoT communication protocol such as CoAP on an IP network over single pair ethernet, but the exact nature of this first proof-of-concept implementation will be determined by the successful applicant themselves.

Suited to: A person with a background in engineering.

Main supervisor: Dr. Robert Howie