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Potential PhD Topics


PhD Supervisors

Below are listed those CAS staff who may be currently looking for PhD students.


PhD Projects

Prof. Matthew Bailes

  • No projects offered at this time

Prof. Chris Blake

  • No projects offered at this time

Dr. Michelle Cluver

  • No projects offered at this time

A.Prof. Jeff Cooke

  • No projects offered at this time

Prof. Darren Croton

  • No projects offered at this time

A.Prof. Adam Deller

Prof. Alan Duffy

A.Prof. Deanne Fisher

  • No projects offered at this time

Prof. Chris Fluke

Prof. Duncan Forbes

  • No projects offered at this time

Prof. Karl Glazebrook

Prof. Alister Graham

Prof. Jarrod Hurley

  • No projects offered at this time

A.Prof. Glenn Kacprzak

  • No projects offered at this time

Prof. Virginia Kilborn

  • No projects offered at this time

Dr. Glen Mackie

  • No projects offered at this time

Prof. Sarah Maddison

Prof. Jeremy Mould

  • No projects offered at this time

Prof. Michael Murphy

  • No projects offered at this time

A.Prof. Emma Ryan-Weber

  • No projects offered at this time

Dr. Ryan Shannon

  • No projects offered at this time

A.Prof. Edward N. Taylor

  • No projects offered at this time

Project Descriptions

The following set of projects have guaranteed external funding:


The missing population of intermediate mass black holes

Supervisor: Prof. Alister Graham

There is a largely-missing population of intermediate-mass black holes (IMBHs) with masses higher than that formed by single stars today (Mbh=1.4 to 120 MSun) and less massive than the supermassive black holes (SMBHs: 105—1010 MSun) known to reside at the centres of big galaxies.  Not surprisingly, astronomers around the world are hotly pursuing the much-anticipated discovery of IMBHs.  This thesis will involve several interconnected projects involving telescope and satellite image analysis and statistical techniques.  Improved methods for estimating both IMBH and SMBH masses will be developed and applied, with ties to the upcoming Large Synoptic Survey Telescope expected.  The coexistence of these massive black holes in dense, compact star clusters at the centres of galaxies is also expected to be a source gravitational radiation detectable by the planned eLISA satellite, for which updated predictions will be made.

Students will benefit from membership in the ARC Centre of Excellence for Gravitational Wave Discovery, OzGrav.

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The following set of projects are subject to a competitive allocation process where only a limited number of scholarships are available:


Astronomical knowledge discovery beyond the petascale

Supervisor: A.Prof. Christopher Fluke

The immensity of data available to modern and future astronomers demands new approaches and techniques for analysis and visualisation. Astronomers are already turning to automated processing using dedicated high-performance computing resources and advanced data archives, supported by machine learning and artificial intelligence. Within this Petascale Astronomy Era, the ability to visualise data will still be crucial for quality control, decision-making, and to support new discoveries. PhD projects are available in one or more of the following areas:

  1. Visualisation Beyond the Desktop: Previous PhD projects have resulted in the development of solutions for real-time visualisation of Terabyte-scale volumes (GraphTIVA), comparative visualisation of volumetric data on a tiled display wall (encube), and advanced graphics shaders (shwirl). In this project, you will extend, combine, improve, and develop new visualisation solutions that support knowledge discovery in astronomy beyond the desktop -- including virtual reality, augmented reality, large-format displays, Gigapixel tiled displays, and cloud-computing services.
  2. Cyber-Human Discovery Systems: This project addresses the question "How do humans and machines work together most effectively to maximise the scientific return of data?" Cyber-human discovery systems combine human-centred visualisation with automated data mining, through strategies such as machine learning and artificial intelligence. At different stages of a discovery process, the amount of work performed by humans and machines can vary -- but this requires an increased understanding of what astronomers do. In this research, you will provide this understanding for a number of astronomical contexts, and create new solutions to support highly customisable, dynamic, visualisation solutions that will sense and enhance the capabilities of humans working closely with machines.

These projects are expected to make use of Swinburne University's Advanced Visualisation facilities - the Enhanced Virtual Reality Theatre and the Swinburne Discovery Wall - and the OzSTAR Supercomputer. These projects will suit students with existing programming skills and interests in data-intensive discovery. Relevant astronomical applications will be selected, offering opportunities for students to collaborate with other researchers in the Centre for Astrophysics & Supercomputing.

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Finding Strong Gravitational Lenses in the High-Resolution imaging era

Supervisors: Prof. Karl Glazebrook and A.Prof. Adam Deller


Strong gravitational lens systems are a corner piece for the study of cosmology, dark matter and galaxy evolution. It has now been established that they can be found very efficiently using convolutional neural networks (a.k.a. 'deep learning') in ground based surveys, searching millions of sources to find hundreds of lenses. In this PhD project we will develop the next generation of machine learning techniques that will be able to detect strong lenses in future high-spatial resolution large imaging surveys. These include orbiting observatories such as Euclid and the Chinese Space Telescope which will survey thousands of square degrees in the optical at resolution similar to the Hubble Space Telescope, and forthcoming SKA and VLBI radio surveys which can deliver resolutions down to milliarcseconds for millions of sources. Key research probems to be addressed are: (i) simulate lensed galaxies at much higher spatial resolution in order to provide realistic training samples, (ii) simulate instrumental effects in the radio domain and (iii) will explore new techniques such as transfer learning and generative adversarial networks to build more adaptable systems.

Image caption: Example of a very high-resolution images of a gravitational lens from Tamura et al. (2015). The thin arc in the RGB image is the cold dust emission from a lensed galaxy at z=3.042 as observed with ALMA in the sub-mm band at 30 milli-arcsecond resolution. The contours show the “low” resolution infrared data from the Hubble Space Telescope superimposed, showing this emission comes from a different part of the galaxy. The galaxy acting as the gravitational lens has been subtracted from this image.

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Dark Matter Visible

Supervisors: A.Prof. Alan Duffy

There exists an unknown invisible mass in the Universe, outweighing everything we can see five times over. This mysterious substance is called Dark Matter, non-interaction with electromagnetism makes it both challenging to directly observe or detection as it is therefore nearly collisionless. Astronomical observations have confirmed the large-scale distribution of dark matter but to determine the particle nature will require measurements on light year scales or event direct detection of the particle itself. Confirming the nature of this new particle is one of the greatest scientific endeavours of our century. Swinburne is part of SABRE, a new experiment one-kilometre underground at a gold mine in Stawell, which will attempt to detect this dark matter.

This project will use high-resolution simulations of Milky Way-like galaxies to explore the potential properties of the dark matter near the Sun's orbit and which SABRE may detect. It will also generate distributions that can inform indirect detection efforts such microlensing, self-annihilation signals and perturbations to the visible sector. A series of zoom-in hydrodynamical simulations of Milky Way type galaxies have been created on the largest supercomputers in the world. Along with these simulations is a new technique to be developed in collaboration with Caltech’s Prof Phil F Hopkins on a more robust numerical estimate for small scale dark matter structures. This research efforts will be used to generate an expectation for the range of dark matter distributions that can inform direct detection experiments. A background/masters in numerical simulations (esp. Gadget/GIZMO) is an advantage as is experience in Python or C.

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Simulating Warm Dark Matter

Supervisors: A.Prof. Alan Duffy

The current dark matter paradigm has been incredibly successful in creating the large-scale structure of the universe we see around us, with simulations explaining the distribution of galaxies across cosmic distances. If you zoom in on smaller scales the simple ‘cold dark matter’ model of a massive particle that has effectively no thermal velocity begins to struggle to explain what we see. From cusp vs core tensions of the inner densities to the missing satellite problem there would appear to be only two options: complex astrophysical processes dramatically alter the dark matter structure on small scales, and / or the dark matter is in fact not perfectly collisionless and ‘cold’, instead it is either self-interacting or has some internal thermal velocity – known as warm dark matter.

Creating the simulations to explore warm dark matter are fraught with numerical instabilities which cause immense difficulties in testing this theory. In this PhD we will explore a new, more rigorous mathematical framing of the theory that will allow us to finally test this highly compelling extension of the dark matter paradigm. Experience in mathematical modelling, python / C / C++ will be advantageous.

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Artist's impression of a black hole. Credit: James Josephides.
Galaxy Structure and massive black holes

Supervisors: Prof. Alister Graham

This project will explore how stars are distributed in galaxy images obtained from both ground-based telescopes and satellites such as Hubble and Spitzer. The structure of galaxies reveals much about how they formed, how they are connected with one another and also with the massive black holes that reside in their cores. A feeling for the type of research done with Prof. Graham can be seen in his Press Releases.

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Testing planetesimal collision models with debris disk observations

Supervisors: Prof. Sarah Maddison

Testing planetesimal collision models with debris disk observations: Planets form through the collisions of asteroid-like bodies. The only way to see these large bodies is via the dust grains they produce via collisions. The student will join an international team conducting the PLATYPUS survey with the Australia Telescope Compact Array to study intrinsic properties of debris disks and test predictions of collisional models of planetesimals.

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