The doctoral candidate will be investigating the power of GS in multiple crops and drive the discovery of trends, statistical models, and machine learning algorithms and their implementation in a plant breeding setting. The species proposed in this study from Puregene and the Molecular Plant Breeding group of ETH Zurich are apple (Malus domestica L. Borkh), common bean (Phaseolus vulgaris L), perennial ryegrass (Lolium perenne L.), buckwheat (Fagopyrum esculentum Moench), wheat (Triticum aestivum L.), and flower as well as hemp type cannabis (Cannabis sativa L).
During the project, the ESR will:
- Develop different statistical models for multiple species with given datasets, which will provide in-depth knowledge on the requirements for datasets to maximize prediction accuracy and how different models work for different data structures
- Establish machine learning approaches and compare them to linear models
- Improve existing datasets using high resolution phenotyping, genome phasing or targeted resequencing of specific genome regions
A planned secondment of nine months at Puregene in Zeiningen, Switzerland is part of this project. Under the supervision of Maximilian Vogt, head of Plant Breeding at Puregene AG, the ESR will be involved in collecting phenotypic (field and glasshouse) and genotypic data. The successful candidate is expected to produce quality research to be published in open-access, peer-reviewed journals relevant to the field, as well as communicate within the network of Puregene.