The Role
Under limited guidance, the candidate will be responsible for:
* Developing machine learning/deep learning models to predict detection, recognition and identification of Australian Defence Force (ADF) platforms using visible images and videos;
* Developing machine learning/deep learning models to generate camouflage schemes using images and videos with the aim to reduce visible detection, recognition and identification of ADF platforms;
* Analysing the detection, recognition and identification of ADF platforms from images and videos;
* Delivering high quality scientific reporting, including briefs, publications and presentations;
* Collaboratively engaging with peers within the Visible, Environmental Signatures & protective Systems group, across Platforms Division and with other divisions;
* Foster and contribute to national and international partnerships;
* Mentoring and nurturing junior staff - developing their military and analytical domain knowledge.
About our Team
The Visible Environmental Signatures and Protective Systems (VESPRS) STC conducts research to develop models and methods to help Defence understand the detectability of our ships, vehicles and field installations. We use physics, AI, art (visual effects for scene simulations) and psychology to create tools to design and test new camouflage strategies, and we work with materials scientists to try to realise these new strategies, so that the ADF can maintain stealth against continuously evolving surveillance threats.
The STC also applies specialist environmental and biological scientific expertise to provide a deep understanding of the biological signature potential of maritime platforms and develop sensors to forecast, detect and manage the signature. VESPRS utilises its sovereign knowledge of the Australian marine environment to develop novel materials and methods for the prevention of biofouling on maritime platforms and provides the ADF with expert, unbiased scientific capability to assess new protection technologies.
The Visible Signatures Discipline focuses on reducing the detectability of land and maritime platforms to visible sensing systems by modelling vessels and vehicles, field measurement experiments, laboratory-based human perception experiments, and through the development of materials to reduce detectability.
Our Ideal Candidate
We are looking for candidates with academic qualifications in a relevant science, mathematics, or engineering discipline. Recent experience in image analysis, pattern recognition and/or object detection would be beneficial. Our Ideal Candidate for the Machine Learning and Data Science position will generally have:
* Experience applying machine learning/deep learning techniques to model complex systems
* Experience using multi-variate data analysis techniques
* Strong communication skills, preferably with experience in presentations, demonstrations, and written communication to industry, academia, or peers
* Experience working in teams to undertake collaborative science and technology projects
* Commitment towards ongoing self-improvement and professional development.
We believe in hiring for potential, we know that some candidates hesitate if they don't tick all the boxes. If this role interests you, but you don't meet all the criteria, we still encourage you to apply.
Application Closing Date: Thursday 18 September 2025
For further information please review the job information pack, reference DSTG/06819/25 on