Ioannis Athanasiadis

Wageningen University & Research is advertising a fully funded PhD position in Foundation Models for Agricultural Sciences within the Artificial Intelligence group. The project sits in the AgriscienceFM initiative and focuses on developing and evaluating domain-specific AI foundation models for agriculture, especially where standard models struggle to generalize across real-world agricultural settings. The research combines computer science , artificial intelligence , machine learning , agricultural sciences , and environmental science . Topics include self-supervised learning , contrastive learning , physics-informed and knowledge-guided ML , remote sensing , climate data , earth observation , time-series analysis , crop type classification, yield forecasting, field boundary delineation, crop disease detection, and crop failure detection. The work involves multi-modal heterogeneous data such as text, images, location data, and time series, and large-scale training on HPC systems. The PhD is embedded in an interdisciplinary and international team led by Prof. Ioannis Athanasiadis , with co-supervision by Prof. Ricardo Torres and Dr. Taniya Kapoor . The position is based in Wageningen, Netherlands , at one of the world’s leading life sciences universities. Eligibility highlights include an MSc in AI, Computer Science, Engineering, or a related field , demonstrated experience in applied machine learning, preferably in remote sensing or agriculture, strong Python skills, and familiarity with PyTorch , Scikit-Learn , or similar tools. Strong writing skills are required, and English proficiency at C1 level is expected. Funding includes a fully funded PhD salary of €3,059 to €3,881 per month over 4 years, plus a tailored training program, pension, year-end bonus, sports facilities, and visa/relocation support. The initial contract is for 18 months and may be extended to the full project duration. Application deadline: 5 May 2026. Applicants should submit a CV, motivation letter, and one scientific writing sample, each limited to 3 pages. Transcripts are not required at this stage, and applications must be submitted via the official WUR vacancy page.

PhD opportunity at Wageningen University & Research in the Netherlands on Trustworthy AI and Causal Inference for Earth Observation . The project sits in the Artificial Intelligence chair and is embedded in the European project PROTEUS , with applications to climate extremes, agricultural disasters, and satellite-based Earth observation. You will work on causal machine learning , uncertainty quantification , out-of-distribution detection , and time-series analysis for multimodal remote sensing data. The research aims to explain crop failure and disentangle the effects of compounding stressors such as heat, drought, and pollution, while building trustworthy AI methods for high-stakes environmental decision-making. Supervision is by Prof. Ioannis Athanasiadis and Dr. Vassilis Sitokonstantinou . The post is based in Wageningen, Netherlands, and includes a fully funded PhD position, tailored training, visa and relocation support, and a salary from €3,059 to €3,881 per month plus an 8.3% year-end bonus . Eligibility highlights include an MSc in AI, statistics, computer science, remote sensing, engineering, or a related field; applied machine learning experience; strong interest in probabilistic ML, causal inference, or time-series; Python and PyTorch/Scikit-learn skills; and English at C1 level. Experience with Earth observation data, Git, and HPC is advantageous. To apply, submit a CV, motivation letter, and one sample of scientific writing, with a maximum of 3 pages total. Transcripts are not required at this stage. The deadline is 11 May 2026 , and first interviews are scheduled for 26 May 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.