Postdoctoral Research Associate In Agricultural Robotics For Vegetable Breeding
vor 21 Stunden
Organisation / Company: UNIVERSITY OF SYDNEY
Research Field: Computer science
Researcher Profile: Recognised Researcher (R2), Established Researcher (R3)
Country: Australia
Application Deadline: 1 Feb 2025 - 00:00 (UTC)
Type of Contract: Other
Job Status: Full-time
Offer Description Located on the Camperdown campus with trips to Victoria for field trials and The Netherlands for additional testing phasesCollaborate with global leaders in vegetable breedingFull time, fixed term for 3 years.
Offering a base salary of $109K - $145K + 17% superannuationAbout the Opportunity ARIAM Research Hub at the University of Sydney, in collaboration with Rijk Zwaan, 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.
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.
Key Responsibilities 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.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.To keep our community safe, please be aware of our COVID safety precautions which form our conditions of entry for all staff, students and visitors coming to campus.
Applications (including a cover letter, CV, and any additional supporting documentation) can be submitted via the Apply button at the top of the page.
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.
Copyright: The University of Sydney
The University reserves the right not to proceed with any appointment.
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