Join a transformative project at RIBES using machine learning and genomics to overcome linkage drag and accelerate crop breeding by design. You will build models, analyse new sequencing data and optimise strategies for combining multiple advantageous traits. Contribute to groundbreaking research shaping the future of sustainable agriculture!
As climate change accelerates and novel plant diseases emerge, global agriculture faces unprecedented challenges. Modern plant breeding – reliant on molecular markers and genomics – often falters when beneficial traits are genetically linked to deleterious ones, a phenomenon known as linkage drag. To overcome this bottleneck, we are launching a transformative project that leverages machine learning to facilitate crop breeding by design.
This project envisions to build a system that enables precise introgression of desirable traits into elite crop varieties by predicting recombination landscapes across a vast number of potential parental crosses. Implementing the project involves working with a variety of large genomics data and later incorporating the testing on known unwanted linkages.
You will primarily be responsible for building a machine learning model and performing computational genomics or quantitative genetics on new sequencing data. Your goal will be to develop a system that will optimise crop breeding strategies to expedite the combination of multiple advantageous traits.
In this position, you will have the opportunity to be part of innovative research on the genetic, molecular, cellular and physiological basis of plant life. You will be embedded in the Plant and Animal Biology department of RIBES.
Essential Qualifications:
- You hold a PhD degree in Bioinformatics, Computational Biology, Plant Genomics or a related field.
- You possess strong programming skills.
= You have hands-on experience with machine learning techniques, particularly in biological or genomic contexts.
Desirable Skills:
- You have experience working with large-scale genomic datasets.
- You are familiar with cloud computing or high-performance computing environments.
- You have knowledge of meiotic recombination, genome analysis, or pan-genomes.
Please apply through our website: www.ru.nl/en/working-at/job-opportunities/postdoc-position-crop-genomics-and-machine-learning