پوزیشن فاند دکتری مهندسی پزشکی اسپانیا

Cancer is a genetic disease, subject to population genetics forces like mutation, selection and stochasticity. Our group is particularly interested in how the evolution and survival of cancer cell populations relies on mutation influx as well as in the selection inference from the observed mutation data. To this end, we develop mathematical and computational approaches to estimate mutation rates and selection. Estimates of the strength of selection in cancer allow for a prioritization of genes and non-coding regions by their disease relevance, with the ultimate goal of promoting therapeutic advances. Coding sequences of cancer tumors not only exhibit positively selected mutations that drive cancer (www.nature.com/articles/s41588-019-0572-y), but there also exists a small fraction of genes that the tumor cannot afford to lose (www.nature.com/articles/ng.3987). In addition to genes, cancer driver loci can occur in the non-coding part of the genome (www.nature.com/articles/s41467-017-00100-x).

We are also interested in mutation rates and selection inference in the context of human genetic variation, including polymorphisms (http://www.nature.com/articles/ng.3831; academic.oup.com/mbe/article-abstract/36/8/1701/5475505) and de novo variants (www.nature.com/articles/s41467-020-17162-z). Here, a particular focus of the group lies on the description of purifying selection in humans and across species, accounting for mutational processes as well as the effects of genetic drift.

The Evolutionary Processes Modeling lab was established in October 2018 and is part of the “Bioinformatics and Genomics” program at the CRG. Further information can be found at https://weghornlab.net/ and at www.crg.eu/en/programmes-groups/weghorn-lab

We are looking for a PhD student to join the lab to help elucidate cancer evolutionary dynamics using population genetics predictions and simulations together with recently published and unpublished cancer sequencing data. Research interests within the field of population genetics but outside this specific topic can also be considered.

The ideal candidate should be highly motivated and eager to work on biological problems using theoretical and computational approaches.

Candidates should have a University Degree and a Master’s Degree in physics, mathematics, statistics, genetics, bioinformatics, computer science or related disciplines within the European Higher Education System (minimum 300 ECTS) or equivalent by September 2021.

The candidate needs to be proficient in English

Funding Notes

Contract duration: Fully funded 4-year PhD position