Computational Scientist in the group ‘Evolutionary Processes Modeling’

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Descripción del trabajo - Computational Scientist in the group ‘Evolutionary Processes Modeling’

The Institute The 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) ( Please, check out our Recruitment Policy: The role We are looking for a research assistant 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. 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 and at Candidates may address scientific enquiries to . Whom would we like to hire?
Professional experience
Must Have You have experience with computer programming You have worked with biological data sets before (e.g. DNA-seq, RNA-seq) Experience with modeling, statistical analysis and/or machine learning is a plus Education and training You should have a master's or PhD degree in bioinformatics, mathematics, physics, computer science or a related discipline Languages You are fluent in English Technical skills You must be able to program in python and/or R Competences You have highly developed organization skills You have good communication skills The Offer – Working Conditions Contract duration: Open - ended contract estimated one year with possibility to extend. Estimated annual gross salary: Salary is commensurate with qualifications and consistent with our pay scales. Target start date: As soon as possible. We provide a highly stimulating environment with state-of-the-art infrastructures, and unique professional career development opportunities. To check out our training and development portfolio, please visit our website in the training section.( We offer and promote a diverse and inclusive environment and welcome applicants regardless of age, disability, gender, nationality, ethnicity, religion, sexual orientation or gender identity. The CRG is committed to reconcile a work and family life of its employees and is offering extended vacation periods and the possibility to benefit from flexible working hours. Application Procedure All applications must include: A motivation letter addressed to Dr Donate Weghorn.
A complete CV including contact details of two referees. All applications must be addressed to Dr. Donate Weghorn and be submitted online on the CRG Career site - Selection Process Pre-selection: The pre-selection process will be based on qualifications and expertise reflected in the candidates' CVs. It will be merit-based. Interview: Pre-selected candidates will be interviewed by the Hiring Manager of the position and a selection panel if required. Offer Letter: Once the successful candidate is identified the People department will send a Job Offer, specifying the start day, salary, working conditions, among other important details. Deadline: Please submit your application by May 31, 2024. Suggestions: The CRG believes in ongoing improvement and promotes a culture of feedback. This is one of the reasons we have in place, at your disposal as a candidate, a mechanism to gather your suggestions/complaints concerning your candidate experience in our recruitment processes. Your feedback really matters to us in our aim at creating a positive candidate journey. You can make a difference and help us improve by letting us know your suggestions through the following form. The contract will be financed by the Plan Estatal PID2021-128976NB-I00.

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