.The InstituteThe Centre for Genomic Regulation (CRG) is an international biomedical research institute of excellence, based in Barcelona, Spain, with more than 400 scientists from 44 countries. The CRG is composed by an interdisciplinary, motivated and creative scientific team which is supported both by a flexible and efficient administration and by high-end and innovative technologies. In April 2021, the Centre for Genomic Regulation (CRG) received the renewal of the 'HR Excellence in Research' Award from the European Commission. This is a recognition of the Institute's commitment to developing an HR Strategy for Researchers, designed to bring the practices and procedures in line with the principles of the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers (Charter and Code).The role We are looking for a Bioinformatician to join the 'Evolutionary Processes Modeling' group. We use computational analysis of sequencing data together with population genetics predictions and statistical modeling to answer questions about mutational processes and selective pressures in cancer tumors. The candidate will analyze data, develop models for the description of tumor DNA sequencing data, develop a computational pipeline and test biological hypotheses. Prior experience with machine learning algorithms is advantageous. The group 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 and in how we can identify cancer driver events from observed mutation data. To this end, we develop mathematical and computational approaches to estimate mutation rates, tumor growth dynamics and selection. Analysis of the mutational processes acting in cancer genomes allows us to learn about carcinogenic mutagens (), while studying the spatial genetic heterogeneity of tumors tells us about the tumor mode of growth (). Estimates of the strength of selection in cancer allow 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 (), but there also exists a small fraction of genes that the tumor cannot afford to lose (). In addition to genes, cancer driver loci can occur in the non-coding part of the genome ().We are also interested in mutation rates and selection inference in the context of human genetic variation, including polymorphisms (; academic.Oup.Com/mbe/article-abstract/36/8/1701/5475505) and de novo variants (). 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
