Ricardo da Silva Torres

Wageningen University & Research is recruiting a Postdoc in Self-Supervised Learning for Image-Based Phenotyping within the chair Artificial Intelligence , led by Prof. Ioannis Athanasiadis , with co-supervision by Prof. Ricardo da Silva Torres and Prof. Athanasiadis. The project sits at the intersection of computer science , agriculture , biology , statistics , and environmental science . The research focuses on self-supervised learning , foundation models , computer vision , and image-based phenotyping for crop improvement. You will work with large-scale, multi-temporal plant image datasets, including UAV and sensor data, to learn biologically meaningful representations of plant traits, stress responses, genetic variation, and genotype-environment interactions. The postdoc is embedded in the PHENOM project and involves collaboration between Wageningen University & Research and Radicle Crops . A key crop in the project is quinoa , used as both a model and target crop for sustainable agriculture and breeding. The role is research-intensive and aims to support predictive breeding pipelines and practical breeding applications. Applicants should have a completed PhD in AI, computer science, statistics, engineering, or a related field, plus strong applied machine learning experience. Experience in Python , PyTorch , Scikit-Learn , Git, and HPC environments is desirable. Background in plant breeding, self-supervised learning, or time-series analysis is a plus. English proficiency at C1 level is expected. The position offers a two-year temporary contract (1+1) with a gross monthly salary of €3,546 to €5,538 for a full-time 38-hour week, with a possible 0.8 FTE arrangement. WUR also highlights benefits such as sabbatical leave, study leave, partially paid parental leave, a year-end bonus, pension, and an international working environment in Wageningen, Netherlands. Applications must be submitted through the WUR website only. The deadline is 8 June 2026 .

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.

Wageningen University & Research is advertising a Postdoc position in Self-Supervised Learning for Image-Based Phenotyping in Wageningen, Netherlands. The project sits at the intersection of computer science , machine learning , computer vision , statistics , agriculture , and biology , with a strong focus on digital phenotyping , foundation models , and plant breeding . The research aims to develop next-generation AI methods for large-scale, multi-temporal plant image datasets, using quinoa as a model and target crop. The work is part of the PHENOM project and is carried out in collaboration with industry partner Radicle Crops . The postdoctoral researcher will design self-supervised learning approaches for UAV and other plant image data, extract robust and interpretable representations of plant traits, and support predictive breeding pipelines. The role includes research, publication, conference presentations, and collaboration with interdisciplinary partners to translate methods into practical breeding applications. The position is embedded in the chair Artificial Intelligence led by Prof. Ioannis Athanasiadis , with co-supervision by Prof. Ricardo da Silva Torres and Prof. Ioannis Athanasiadis . Applicants should hold a PhD in AI, computer science, statistics, engineering, or a related field, and have experience in applied machine learning. Skills in Python, PyTorch, Scikit-Learn, Git, and HPC are desirable. Experience with plant breeding or self-supervised learning is a plus. English proficiency at C1 level is expected. The appointment is for 2 years (1+1) with a gross monthly salary of €3,546–€5,538 for a full-time 38-hour week, plus standard WUR employment benefits. Applications must be submitted through the official WUR website. Deadline: 23 June 2026.