Postdoctoral Fellow, Object Detection

Canberra, Australia
02 Mar 2021
End of advertisement period
31 Mar 2021
Contract Type
Full Time

Classification: Academic Level A
Salary package: $73,309 - $92,015 per annum plus 17% superannuation.
Terms: Full-time, Continuing (Contingent Funded)

  • Join a diverse multidisciplinary team working across domains and scales to build a future science platform.
  • Engage in collaborative research, based at the CSIRO, to solve significant science questions through analytical advances.
  • Gain experience in the development of cutting-edge statistics, machine learning and artificial intelligence applied to real-world problems in biology, agriculture and aquaculture.

Position overview

The Biological Data Science Institute (BDSI) is a academic unit in the College of Science that sits at the interface of data science and biological science. It aims to recruit, build and coordinate expertise in biological data science to accelerate the translation of biological data to biological knowledge. Operating in the space between traditional disciplines, the BDSI is positioned to collaborate across the ANU campus and with partner organisations to solve problems that have impact.

Multiple BDSI positions are being recruited in formal partnership with CSIRO to support their Future Science Platform in Machine Learning and Artificial Intelligence (MLAI FSP). This cohort will work with top scientists and engineers to develop, extend and leverage MLAI approaches to advance analytical frontiers in areas with direct applications to animal and plant breeding. The Postdoctoral Fellows will be embedded in a collaborative FSP research team at the CSIRO Black Mountain site adjacent to the ANU Acton campus, while also being members of the ANU Biological Data Science Institute.

These positions are being recruited into the “Object Detection” activity within the MLAI FSP. The Postdoctoral Fellows will focus on the development of new machine learning algorithms and research to tackle problems relevant to Agriculture, Aquaculture and Livestock industries.

These problems require new solutions to:

  1. bypass the limitations of hand-labelling data by incorporating domain knowledge or synthetic data;
  2. integrate data from multiple streams with differing dimensionality (e.g. images, videos, 3D data, or non-imaging sensors); and,
  3. capture signals in the temporal domain. In contributing to these solutions, the Postdoctoral Fellow will work at the interface between machine learning research and specific domain applications, and will interact with experts in both areas.

There is funding to support this position for three years.

For further information, please contact Professor Eric Stone E:

Closing Date: 31 March 2021

Position Description: PD _Postdoctoral Fellow (Object Detection)_ Level A.pdf

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Application information

In order to apply for his role, please make sure that you upload the following documents:

  • statement addressing the selection criteria, and
  • A current curriculum vitae (CV).

Applications which do not address the selection criteria may not be considered for the position.

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