[ASA] Two CSIRO Postdoctoral Fellowships in Machine Learning Methods for Radio Astronomy

[Huynh, Minh (CASS, Kensington WA)]

Dear ASA members,

CSIRO Astronomy and Space Science are seeking applications for two postdoctoral positions in machine learning methods for radio astronomy within our new Machine Learning/Artificial Intelligence Future Science Platform. The ML/AI FSP will bring together a large number of world-leading experts to explore scientific questions using machine learning techniques, and the successful candidates will have the opportunity to work on projects ranging across multiple science research areas.

Details below. Please forward the following information to prospective candidates and encourage them to apply.

Many thanks,
Minh

*** Finding the Unknown in Radio Astronomy Datasets ***

This position is focused on identifying rare and correctly unknown classes of astrophysical objects in Parkes and ASKAP data. The EMU survey on ASKAP will increase the number of known radio sources from millions to 10s of millions. The new receiver systems on Parkes are undertaking new deep and wideband surveys for pulsars and fast radio bursts. The postdoc will hunt for astrophysical sources in these large datasets.

Location: Marsfield, Sydney
More Information and how to apply:
https://protect-au.mimecast.com/s/w41kC91WPRT8Vn6oTojbUa?domain=jobs.csiro.au
Contact: George.Hobbs at csiro.au<mailto:George.Hobbs at csiro.au>

*** Classifying Radio Galaxies in Large Surveys ***

This position is focused on applying machine learning methods to radio imaging from ASKAP telescope. The EMU survey on ASKAP will increase the number of known radio sources from millions to 10s of millions. The success of EMU relies on machine learning techniques to find patterns and objects in the radio images, and thereby automatically classify radio galaxies and their multiwavelength counterparts. You will work at the interface of astrophysics and machine learning to enable cutting edge astrophysics.

Location: Kensington, Perth
More Information and how to apply:
https://protect-au.mimecast.com/s/q2RJC0YKPvij0v96TDVr1Y?domain=jobs.csiro.au
Contact: Minh.Huynh at csiro.au<mailto:Minh.Huynh at csiro.au>

*** Deadline for both:  Sunday 21 June, 2020  ***

Essential selection criteria for both include:
•              A doctorate (or will shortly satisfy the requirements of a PhD) in a Platform-relevant discipline area, such as computing, astrophysics or physics. Please note: To be eligible for this role you must have no more than 3 years (or part time equivalent) of postdoctoral research experience.
•              A sound history of publication in peer reviewed journals and/or authorship of scientific papers, reports, grant applications or patents.
•              Solid knowledge of machine learning techniques and proven ability to develop and apply novel machine learning techniques to complex data sets.
•              The ability to work effectively as part of a multi-disciplinary, regionally dispersed research team, plus the motivation and discipline to carry out autonomous research.
•              Knowledge of Python, Julia, C, C++ or equivalent.

CSIRO Astronomy and Space Science is committed to building a safe and welcoming workplace culture, and to implementing initiatives to improve diversity and equity within our workplace. CSIRO offers a range of flexible working arrangements to support these initiatives. A discretionary research/travel allowance will be available, together with assistance with relocation and other benefits, including health insurance. We work flexibly at CSIRO, offering a range of options for how, when and where you work (through our 'Balance' programme).  Talk to us about how this role could be flexible for you.   We offer a vibrant and collaborative work environment. CSIRO actively supports a safe, healthy and enriching working environment for all staff.


-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.sydney.edu.au/pipermail/asa/attachments/20200513/cbefd2b7/attachment.html>