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|Selecting the best plants from large populations is one of the most important processes in the plant breeding practice. The selection process to obtain the best genotype typically spans multiple rounds of selection in successive plant populations. This combination of iteration and the stochastic element of mendelian allele segregation makes the result of even a relative simple selection process surprisingly difficult to predict. For a plant breeder, it would be of large benefit, if an optimal selection process could be determined in advance, as growing and selecting large populations of plants is costly. In the context of a challenging research program in collaboration with three breeding companies, KeyGene is developing and testing algorithms to predict optimized breeding strategies. (more)|
PhD-position in Computational Microbiology
Rapid diagnosis of drug resistant TB infections
Despite treatment options, TB is killing 1.5 million people each year, the vast majority in developing nations. Current diagnostics often take several weeks or months, which is too long to save the lives of the patients or to prevent further transmission. This project collaborates with Dr. Pym of the K-RITH research institute in Durban South-Africa to develop an innovative rapid diagnostic that accurately identifies currently undiagnosable TB infections, i.e. mixed and drug resistant infections. This will be done by computationally identifying markers that are indicative of these infections in genomic data which are then used to design the diagnostic using existing sequencing technology.
Your objectives in this project are to identify novel drug-resistance conferring mutations, to create a predictive model for drug resistance and mixed infections in TB and to explore the options to turn these insights into a prototype point-of-care molecular diagnostic. We have a collection of sequencing and meta-data from >8000 diverse TB strains. We now have the unique opportunity to leverage these data to make a global societal impact. You will integrate public and private data sets to associate mutations with phenotypes, such as drug resistance, with state-of-the-art computational approaches.
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