Computational Scientist In The Group 'Evolutionary Processes Modeling

Detalles de la oferta

Computational Scientist in the group 'Evolutionary Processes Modeling Deadline: 30/11/2024 Centre / Institution: Center for Genomic Regulation 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 of an interdisciplinary, motivated, and creative scientific team which is supported by a flexible and efficient administration and high-end innovative technologies.
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.
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Salario Nominal: A convenir

Fuente: Talent_Dynamic-Ppc

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