Postdoc in Self-Supervised Learning for Image-Based Phenotyping and AI Foundation Models Wageningen University & Research in Netherlands

Wageningen University & Research is advertising a postdoc position in self-supervised learning for image-based phenotyping, with a strong focus on AI foundation models, plant phenotyping, and crop improvement. The project sits within the chair Artificial Intelligence and is embedded in the PHENOM project, in collaboration with Radicle Crops and WUR.

The successful candidate will design and develop self-supervised learning methods for large-scale, multi-temporal plant image datasets. The work aims to extract robust and interpretable representations of plant traits from UAV and field-trial data, supporting predictive breeding pipelines and practical agricultural applications. The post specifically mentions work with quinoa as a model and target crop, and uses data from NPEC.nl and field trials from RADICLE Crops.

Academic supervision is by Prof. Ioannis Athanasiadis and Prof. Ricardo da Silva Torres. The role involves research on state-of-the-art self-supervised learning and foundation models, model development for plant image datasets, dissemination through publications and conferences, and collaboration with interdisciplinary partners.

Eligibility highlights include a completed PhD in artificial intelligence, computer science, statistics, engineering, or a related field; demonstrated applied machine learning experience; and desirable background in computer vision, time-series analysis, plant breeding, or self-supervised learning. Strong Python skills and familiarity with PyTorch, Scikit-Learn, Git, and HPC environments are advantageous. English proficiency at C1 level is expected.

The position is a temporary two-year contract (1+1) with a gross monthly salary of €3,546–€5,538 for full-time work (38 hours/week), with a possible 0.8 FTE arrangement. WUR also highlights benefits such as sabbatical leave, study leave, partially paid parental leave, pension, and a year-end bonus.

The application deadline is 2026-06-08, and first interviews are scheduled for 2026-06-22. Applications must be submitted through the WUR vacancy page.