Fixed-term: The funds for this post are available for 6 months in the first instance. We are seeking a Research Associate to join the PhenoEye Project at the University of Cambridge's Department of Computer Science and Technology. We seek a highly motivated Postdoctoral Research Associate to join a research lab working on developing AI-powered digital phenotyping technology for plant breeding. The project applies advanced computer vision and machine learning techniques to create high-fidelity digital twins of crops, enabling automated trait measurement. The technology is being developed in collaboration with leading international breeding companies and research institutions, with the goal of transforming how variety selection and crop monitoring are conducted in commercial plant breeding. This position will involve conducting independent and collaborative research that advances the state of the art in AI-powered digital phenotyping technology for plant breeding. The successful candidate will hold (or be close to completion) a PhD in Computer Science, Computer Vision, Machine Learning, or a closely related discipline, or equivalent research or industry experience. They will be self-motivated, solutions-oriented, and have a solid understanding in the relevant disciplines. The role holder is expected to have a track record of research in a subset of the following topics: Computer Vision Strong programming skills, particularly in Python and deep learning frameworks 3D reconstruction techniques (highly desirable) Peer-reviewed publications in relevant venues Applicants should contact Professor Cengiz Oztireli ( aco41@cam.ac.uk ) for further information about the position. Appointment at Research Associate level is dependent on having a PhD. Those who have submitted but not yet received their PhD will be appointed at Research Assistant level, which will be amended to Research Associate once the PhD has been awarded. Further information on the requirements for the role can be found in the further particulars. In addition to the base salary stated above, the successful candidate for this post will receive an additional 2.5% supplement to their pay. Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online. Please provide a CV and covering letter. If you upload any additional documents which haven't been requested, we will not be able to consider these as part of your application. Questions about the recruitment process may be addressed to the HR Team at hr-admin@cst.cam.ac.uk Please quote reference NR49646 on your application and in any correspondence about this vacancy. The University actively supports equality, diversity and inclusion and encourages applications from all sections of society. The University has a responsibility to ensure that all employees are eligible to live and work in the UK.