About the position

The Australian Plant Phenomics Network (APPN) was established in 2009 under the Australian Government’s National Collaborative Research Infrastructure Strategy (NCRIS) to develop a globally collaborative plant phenomics capability that provides leadership in infrastructure, skills and service for agricultural research and industry to maximise the productivity in Australia’s unique and variable environment. In January 2024, La Trobe University joined as a new APPN Node, providing advanced plant phenomics services to academia and industry. The La Trobe APPN Node will exist within a cooperative national network of nine nodes that are coordinated from the APPN Central at the Adelaide University. The role requires a data scientist with experience in predictive modelling using non-invasive plant phenotyping data (multi-/hyperspectral sensor outputs).

Skills and Experience

To be considered for this position, you will have;

Completion of a tertiary qualification related to image-focused machine learning, with at least 3 years of relevant experience, or an equivalent combination of education and/or training, including broad experience in a scientific teaching and/or research and/or industry environment(s).
Experience in the operation, maintenance and troubleshooting of specialised imaging infrastructure, especially multi/spectral cameras.
Experience in the maintenance and use of high-performance computing (HPC) systems, including Linux, shell, slurm and/or PBS.
Excellent interpersonal skills with the demonstrated ability to liaise and work with diverse groups of stakeholders, including staff at senior levels, researchers, technical staff, students, and other organisations.
Capacity to work independently as well as in a team environment.
Demonstrated high level of proficiency with written and spoken English, including complex system documentation.
Demonstrated expert knowledge in building and deploying machine learning algorithms for data and/or multi-/hyperspectral image analysis using supervised and unsupervised deep learning techniques.
Demonstrated expert knowledge in programming languages and standards such as Python, R, SQL and/or JSON, and database management systems, including object stores or data lakes, that underpin contemporary research activities
Demonstrated expert experience in data visualization for researchers, stakeholders and/or enterprise reporting and analytics (e.g. python or R libraries, PowerBI, Tableau or similar).
Demonstrated experience in configuring and administering both SQL and Non-SQL databases and building automated pipelines around these.

Other requirements;

Comply with the Australian Government Department of Health, Office of Drug Control guideline standards for Fit and Proper Persons and Suitable Staff https://www.odc.gov.au/; AND
Undertake a current (within the last 12 months) national police check; AND
The position will involve sponsored research with industry partners requiring the employee to agree to confidentiality clauses as well as assignment of Intellectual Property (IP) rights to the University. While this will not prevent publication, it may cause some delays as processes to protect IP are implemented.

Please refer to the Position Description for other duties, skills and experience required for this position.

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Research Associate – Plant Phenotyping