Located on the Camperdown campus with trips to Victoria for field trials and The Netherlands for additional testing phases
Collaborate with global leaders in vegetable breeding
Full time, fixed term for 3 years. Offering a base salary of $109K - $145K + 17% superannuation
About the opportunity
ARIAM Research Hub at the University of Sydney, in collaboration with Rijk Zwaan, a global leader in vegetable breeding, seeks to transform breeding operations through the integration of advanced robotics and digital twin technologies. We invite applications for two Postdoctoral Research Associate positions to lead innovative research in robotics, advanced sensing, and optimisation, with a specific focus on AI-driven phenotyping and digital twin modelling to enhance crop traits and breeding efficiency.
These positions are dedicated to both fundamental research and practical implementation in robotics, advanced sensing, AI, and optimisation within the context of vegetable breeding.
You will integrate sensing, sampling, and optimisation technologies into a cohesive system, engaging directly with Rijk Zwaan farm managers and breeders to address practical challenges, such as robot adaptation to specific site conditions and targeting key phenotypic traits. You will be based at the University of Sydney, and will conduct field trials primarily in Victoria, with additional testing phases in the Netherlands.
This collaborative framework ensures that the research is both technically rigorous and practically transformative, bridging the gap between academia and industry to deliver impactful agricultural robotic solutions.
These positions are dedicated to both fundamental research and practical implementation in robotics, advanced sensing, AI, and optimisation within the context of vegetable breeding.
Digital Phenotyping Lead
deploy and integrate RGB, multispectral, and MWIR sensors on autonomous ground robots for real-time phenotyping, enabling comprehensive data capture of plant traits
incorporate a smart soil-sampling module on the ground robot for real-time soil property assessment, providing critical below-ground data to complement above-ground phenotypic analysis
develop machine learning algorithms for real-time analysis of plant, soil, and environmental data, supporting rapid decision-making in breeding processes
integrate multi-modal data (soil, environmental, and above-ground traits) for a holistic assessment of crop health and performance
collaborate closely with Rijk Zwaan breeders to align sensing technologies with key phenotypic traits, ensuring that research outputs are actionable and directly support breeding objectives.
Digital Twin Lead
develop predictive algorithms to identify optimal robotic sampling points, maximising phenotyping efficiency and reducing redundancy
design and implement digital twin models that integrate soil, crop, and environmental data, enabling simulation of growth scenarios and predictive analysis of breeding outcomes
refine robotic sampling strategies to improve precision and efficiency, leveraging insights from digital twin simulations to enhance data quality and decision-making processes
adapt digital twin models and robotic sampling protocols to various operational environments, collaborating with Rijk Zwaan farm managers and breeders to ensure practical applicability and robustness of data collection
validate and iteratively refine digital twin models using field data to ensure accuracy, reliability, and relevance to breeding programs.
Core Challenges
translating complex research objectives into reliable, deployable systems suitable for real-world agricultural settings
coordinating multidisciplinary teams across multiple geographic locations to achieve seamless integration and implementation
collaborating with industry stakeholders, including farm managers and breeders, to align research outcomes with operational goals
balancing the pursuit of innovation with the need for scalability and commercial viability, ensuring that research outcomes are both advanced and applicable to industry challenges.
About you
The University values courage and creativity; openness and engagement; inclusion and diversity; and respect and integrity. As such, we see the importance of recruiting talent aligned to these values. We are seeking candidates with a strong technical background and a passion for impactful research and innovation. Key qualifications include:
PhD in robotics, computer science (real-time AI systems, real-time optimisation), computer vision, advanced remote sensing
expertise in autonomous systems, sensing technologies, or digital twin frameworks.
strong leadership and project management abilities, with demonstrated success in managing collaborative research projects
experience working with industry partners and international collaborators to align research initiatives with practical and commercial needs.
Specific Expertise:
Digital Phenotyping Lead: Expertise in sensing technologies, computer vision, machine learning, and robotics, specifically within a real-time field context.
Digital Twin Lead: Expertise in complex systems modelling for digital implementation, optimisation algorithms, and/or automated sampling methodologies, with a focus on real-time implementation.
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EEO statement
At the University of Sydney, our shared values include diversity and inclusion and we strive to be a place where everyone can thrive. We are committed to creating a University community which reflects the wider community that we serve. We deliver on this commitment through our people and culture programs, as well as key strategies to increase participation and support the careers of Aboriginal and Torres Strait Islander People, women, people living with a disability, people from culturally and linguistically diverse backgrounds, and those who identify as LGBTIQ. We welcome applications from candidates from all backgrounds.
How to apply
Applications (including a cover letter, CV, and any additional supporting documentation) can be submitted via the Apply button at the top of the page.
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For a confidential discussion about the role, or if you require reasonable adjustment or any documents in alternate formats, please contact Rebecca Astar or Cherie Goodwin, Recruitment Operations by email to recruitment.sea@sydney.edu.au.
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Applications Close
Saturday 01 February 2025 11:59 PM
