PhD in AI-driven gene and trait discovery for plant breeding Wageningen University & Research in Netherlands

Wageningen University & Research is advertising a fully funded PhD position in AI-driven gene and trait discovery for plant breeding. The project sits in the Department of Plant Breeding and is linked with collaborators at Aarhus University, Wageningen Plant Research, and other Wageningen Plant Sciences groups.

The research focuses on combining machine learning, multi-omic data analysis, genetics, genomics, quantitative trait analysis, and high-throughput phenotyping to uncover molecular drivers of complex traits in crops. A key early application is integrating transcriptome, proteome, and metabolome data at single-cell level in faba bean, then linking these data to plant traits. The PhD will develop interpretable and biologically grounded AI models, compare them with GWAS/eGWAS approaches, and study the genetic architecture of complex traits across biological scales.

Supervision will be led by Prof. Agnieszka Golicz at Wageningen University & Research. The post is ideal for an enthusiastic, independent researcher who enjoys noisy biological data, interdisciplinary teamwork, and method development across bioinformatics, molecular biology, plant development, stress responses, and phenotyping.

Eligibility highlights include a completed MSc in plant sciences, biology, agriculture, genetics, bioinformatics, artificial intelligence, or a related field; experience with multi-omic data, association studies, or biological process modelling; strong quantitative analysis skills; and an interest in linking genotype to phenotype. English proficiency at C1 is expected, and an internationally recognised English certificate may sometimes be required.

Funding includes a salary starting at €3,059/month in year 1 and rising to €3,881/month in year 4, plus standard Dutch university employment benefits. The contract is initially for 18 months and may be extended for the project duration if performance is good.

Deadline: 8 June 2026. Apply via the WUR vacancy page with a CV and motivation letter only.