We are looking for
The PhD project ‘Prediction of Candidate genes for Traits using interoperable genome annotations and literature’ aims at solving important questions in plant crop species: which gene (networks) are candidate for traits of interest and should be introgressed to breed improved cultivars by incorporating knowledge from previous QTL studies, available in literature, with annotations from de novo assembled genomes.
In this project, the goal is to develop statistical, genetics, text mining and bioinformatics tools for extracting breeding information from literature, utilizing computational (semantic web) approaches to combine this information with knowledge from other resources, further develop the correct ontologies to do so, and reasoning (knowledge discovery) to prioritize likely candidate gene (networks). Can we devise strategies which lead to faster discovery of candidate genes for traits of interest to plant breeders?
You will work in close cooperation with PhD students and researchers involved in (related national) projects on the development data integration. The project team includes an eScience engineer of the NWO eSience center. You will be in regular contact with plant breeding companies
We are looking for an ambitious PostDoc with an PhD Degree in Bioinformatics, Statistics, Plant Sciences. The candidate is highly motivated to develop a scientific career and is able to perform independent research, but values collaboration with other PhD students and researchers. The candidate is fluent in English and has excellent communication, scientific writing and presentation skills. For this position in particular, the candidate should in particular demonstrate:
- appropriate knowledge of genetics, genomics, bioinformatics and statistics
- being able to work in a highly multi-disciplinary team
- a good understanding of semantic web technologies, data interoperability and FAIR data principles
- Experience with machine learning / Analysis of Big Datasets
- proficiency in the bioinformatics analysis, interpretation and scientific visualization of large data sets
- proven proficiency in programming
- knowledge of and experience with statistics software