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Vacation Scholarships in Astronomy at CAS

The Centre for Astrophysics & Supercomputing (CAS) accepts applications for Vacation Scholarships from enthusiastic university students with excellent scholastic records who are in the last, or second last, year of their undergraduate or Honours/Masters degree.

With 16 research faculty and more than 30 post-docs and PhD students, CAS is a vibrant, friendly environment for studying most fields of astronomy. Swinburne astronomers have guaranteed access to the twin Keck 10-m Telescopes in Hawaii - the world's premier optical observatory - and CAS owns and operates one of Australia's most powerful supercomputers - the Green & Gstar Machines . We also develop advanced immersive 3D data visualization facilities and create 3-D animations and movies promoting and explaining astronomy to the broader community.

Swinburne's Hawthorn campus is situated in a lively, urban setting just minutes by public transport from Melbourne's city centre.

Our Vacation Scholarship program aims to provide undergraduate students with some insight into how exciting research is and how it is conducted. Students will join a research project, or maybe help start a new one, in one of the many areas of astronomy in which CAS staff and post-docs are experts. The various projects on offer are listed below. Projects can involve all aspects of astronomical research, from proposing or carrying out new telescope observations, to analysing some of the data or conducting theoretical calculations or advanced simulations. Many previous students have eventually published peer-reviewed research articles on some of their Vacation Scholarship research.

Applications can be made at any time throughout the year. We particularly encourage applicants to work over the summer months, December to February.

This program is open to undergraduates at Australian & New Zealand universities. Applications from students outside of Australia & New Zealand with exceptional scholastic records may also be considered.

Scholarships will generally last between 6 and 10 weeks, to be negotiated between the student and their nominated supervisor. Vacation Scholars are paid a tax-free stipend of $500 per week.

Applications should include the following:

  • A cover letter (see below for further information);
  • A copy of your official academic record, including an explanation of the grading system used;
  • Your Curriculum Vitae;
  • Any supporting documentation of previous research.

Applicants should also ask a lecturer or supervisor at their current university to send a letter of recommendation. This should be sent by the lecturer/supervisor directly; applicants should not include reference letters in their own application.

Applications and reference letters should be emailed to Dr. Ewan Barr ( with the above information attached (preferably as PDF documents).

The cover letter is important and should
(i) set out why you are interested in undertaking a vacation scholarship at Swinburne and
(ii) list at least two research projects you are interested in working on. See below for the current list of projects on offer.

Potential Vacation Scholarship research projects

The following list outlines particular projects currently on offer. Contact the staff member(s) listed for more information. Other projects, not listed here, may be possible; contact the staff member whom you feel is most suited to your ideas and discuss other possible projects of mutual interest.

(Updated 26/02/2015)
  • Understanding the nature of sub-millimeter galaxies
    The stellar masses of sub-millimeter galaxies (SMGs) have been a subject of debate in the community, and are crucial to understand if these galaxies are part of the so-called 'main sequence' of star-forming galaxies (i.e. the tight relation between stellar mass and star formation rate exhibited by the majority of normal star-forming galaxies at a given redshift), or if they are outliers of this relation, as is the case for mergers undergoing a major starburst in the local Universe. The stellar masses of SMGs are challenging to constrain because of their optical faintness and also because of great degeneracies between star formation histories and dust attenuation properties in galaxies that affect the spectral energy distribution modelling. Different methods have led to large differences in the stellar masses of one of the most widely used SMG samples in the literature (Chapman et al. 2005) obtained by two different groups (Hainline et al. 2010 and Michalowski et al. 2011), and the different stellar masses imply different specific star formation rates (and ultimately star formation modes) for these sources.
    In da Cunha et al. (2015), we present a new version of the MAGPHYS spectral energy distribution code (da Cunha et al. 2008) that is optimized to obtain the properties (including stellar masses and star formation rates) of SMGs, dealing with degeneracies using a Bayesian approach that produces the most robust estimates of physical parameters possible. This model was applied to a new sample of SMGs observed with ALMA (ALESS; Hodge et al. 2013). The student will apply this model to the SMG sample of Chapman et al. (2005), in order to settle the debate between Hainline et al. and Michalowski et al. regarding the properties of these galaxies. The student will use the same (public) multi-wavelength data as used by the other studies, and obtain stellar masses and star formation rates for all galaxies using MAGPHYS. Then, the student will compare these properties with those derived by the previous studies and interpret this comparison in the context of the different modelling approaches. Finally, the results obtained by the student will be used to compare the properties of the Chapman et al. (2005) sample of SMGs with those from ALESS sample obtained using the same modelling approach.

    Desired skills:

    • strong background in galaxy evolution, stellar populations and interstellar medium
    • excellent spoken and written communication
    • good programming skills (knowledge of Fortran/C/IDL languages)
    • good understanding of Bayesian statistics

    Supervisor: Dr. Elisabete da Cunha.