We are seeking highly skilled Postdoctoral Research Associates to lead innovative research in robotics, advanced sensing, and optimisation, focusing on AI-driven phenotyping and digital twin modelling to enhance crop traits and breeding efficiency.
The successful candidates will integrate sensing, sampling, and optimisation technologies into a cohesive system, engaging directly with farm managers and breeders to address practical challenges. 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.
This project involves deploying and integrating RGB, multispectral, and MWIR sensors on autonomous ground robots for real-time phenotyping, incorporating a smart soil-sampling module, and developing machine learning algorithms for real-time analysis of plant, soil, and environmental data. The ultimate goal is to create a holistic assessment of crop health and performance using multi-modal data.
We are looking for individuals with expertise in autonomous systems, sensing technologies, or digital twin frameworks, strong leadership and project management abilities, and experience working with industry partners and international collaborators.
Key qualifications include a PhD in robotics, computer science, computer vision, or advanced remote sensing, as well as a proven track record in designing and implementing complex systems models for digital implementation and optimisation algorithms.