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 address significant science questions through analytical advances.
- Gain experience in the development of 3D computer vision, machine learning and artificial intelligence applied to real-world problems in biology, agriculture and aquaculture.
Position overview
The Biological Data Science Institute (BDSI) is an 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.
This position is being recruited in formal partnership with CSIRO to support their Future Science Platform in Machine Learning and Artificial Intelligence (MLAI FSP). In this role, the successful candidate 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. They will be embedded in a collaborative FSP research team at the CSIRO Black Mountain site adjacent to the ANU Acton campus, while also being a member of the ANU Biological Data Science Institute.
The Postdoctoral Fellow will contribute to the “Object Detection” activity within the MLAI FSP, focusing on the development of novel 3D computer vision, machine learning and artificial intelligence approaches to tackle problems relevant to Agriculture 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: director.bdsi@anu.edu.au or Dr Vivien Rolland E: vivien.rolland@csiro.au
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Application information
In order to apply for his role, please make sure that you upload the following documents:
- A statement addressing the selection criteria.
- A current curriculum vitae (CV) which includes the names and contact details of at least three referees (preferably including a current or previous supervisor). If your CV does not include referees you can complete these online when prompted in the application form.
- Other documents, if required.
Applications which do not address the selection criteria may not be considered for the position.
Please note: The successful candidate will be required to undergo a background check during the recruitment process. An offer of employment is conditional on satisfactory results.
Advertised: 01 Jun 2022 09:00:00 AM AUS Eastern Standard Time
Applications close: 20 Jul 2022 11:55:00 PM AUS Eastern Standard Time
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