Laureate Data Scientist

Location
Hawthorn, Australia
Posted
26 Oct 2018
End of advertisement period
31 Jan 2019
Ref
2034_10/18_A1
Contract Type
Fixed Term
Hours
Full Time
  • Centre for Astrophysics & Supercomputing
  • Academic Level A or B depending on experience
  • 5 year fixed term contract, Hawthorn location

A five year data scientist position is available at Swinburne University of Technology to work with Prof. Karl Glazebrook on galaxies in the early (z>4) Universe.

This position is funded as part of Prof. Glazebrook’s Australian Research Council Laureate Fellowship. This is a $4M project whose goal is to understand the formation of the first massive galaxies in the Universe using the forthcoming James Webb Space Telescope (JWST) and supporting ground and space-based surveys. You will be joining a team of up to 9 scientists working on the Laureate project. 

About Swinburne

Swinburne is a progressive university that aims to increase Australia’s capacity in science, technology and innovation as the drivers of modern, internationalised economies and workplaces. Our university is focused on high-impact global research, high-quality teaching and active engagement with both industry and the community. Swinburne houses an active astronomy group, which is one of Australia's largest astronomy centres. Swinburne astronomers have institutional access to W.M. Keck telescopes in Hawaii, and regularly obtain observing time on the AAT, ESO telescopes, HST, Magellan, ATCA and Parkes telescopes. The Centre has OzSTAR, the Australian GPU Supercomputer for Theoretical Astrophysics, available in-house, as well as access to state-of-the-art 3D visualisation facilities.

About the job

We are looking for a suitably qualified candidate to fulfil a 5 year year postdoctoral research position for the purpose of conducting research in collaboration with Prof. Karl Glazebrook. As our Laureate Data Scientist, you will support our team with advanced machine learning and data analysis techniques, with a particular emphasis on the application of modern neural network architectures. Your work will enable the team to develop machine learning pipelines for automated object discovery in deep multi-band images, automated classification and redshift measurement and automated classification of exotic objects.

About you

The position can be at Level A or B depending on prior experience. The appointment will be for 5 years, starting in August 2019, and will be within the Centre for Astrophysics & Supercomputing (CAS). 
To be successful in the role, you will have:

  • A PhD in astronomy, astrophysics or computer science (submitted at start of position)
  • A strong track record of research as demonstrated by publication
  • Experience with machine learning techniques (such as neural networks, random forests, support vector machines)
  • Knowledge of astronomical fundamentals (at undergraduate level)

A full list of the selection criteria is available within the position description

Benefits

Swinburne values its diverse work environment and is committed to the principles of equal opportunity and cultural diversity. The University has been recognised as a Workplace Employer of Choice for gender equality for the seventh year. Swinburne recognises the importance of providing career development, and offers promotion opportunities for both continuing and fixed-term staff.

The University also offers a range of employee benefits including maternity and partner leave, a 17% employer superannuation contribution, 4 weeks’ annual leave and discounted health insurance. Swinburne supports a flexible working environment; subject to visa restrictions, this position is available either full-or part-time.
Swinburne University of Technology is a Child Safe Organisation and as part of this commitment, all appointments are subject to a valid Working with Children Check.

More information: 

http://www.swinburne.edu.au/corporate/hr/swin/benefits/index.htm

Application process

To view the position description or to start an application click on 'apply' at the bottom of this page and submit a resume, cover letter and response to the Key Selection Criteria, as listed in the Position Description.

Shortlisting for this role will occur in early February 2019, and at that time shortlisted candidates will be asked for a Letter of Recommendation prior to interview. Our anticipated start date for this role will be August 2019.

Applications close at 5 pm Wednesday 30th January 2019 (AEST)