PhD in Foundation Models for Agricultural Sciences at Wageningen University & Research Wageningen University & Research in Netherlands

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.