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 supported 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.
Please, check out our Recruitment Policy.
The role The overarching theme of the research in our lab is the understanding of the information encoded in genomic sequences and how this information is processed in the pathway leading from DNA to protein sequences.
More specifically, we are interested in the epigenetic regulation of gene expression and RNA processing, the relationship between molecular phenotypes and higher order endophenotypes and organismal phenotypes, and the identification of functional regions on the genome of all living beings.
Our group is mostly computational, and we do both large-scale data analysis and development of methods, but it has also an important experimental component.
We participate in many large scale international functional genomics projects, such as ENCODE, GTEx, BluePrint and others.
PrecisionTox is a European funded project, the overarching aim of which is to establish a new, 3Rs-compliant (Replacement, Reduction, Refinement), cost-effective testing paradigm for chemical safety assessment — Precision Toxicology — that revolutionizes regulatory toxicology, replaces animal testing, reduces uncertainty, and determines safety factors in assessing risks to human health.
This goal will be accomplished by identifying molecular key event (KE) biomarkers, predictive of chemically induced adverse health effects in humans, that feed directly into regulatory and industry practice via the systematic use of distantly related animal species from across the tree of life and the highly interdisciplinary mix of genomics, metabolomics, evolutionary theory, quantitative genetics, data science, toxicology, and law.
Within this project, our group at the CRG focuses on the analysis of bulk and single cell RNA-seq from five different model species (Danio, Xenopus, Caenorhabditis, Daphnia and Drosophila) after treatment with selected putative cardio, neuro, and hepatotoxic compounds emerging from other efforts within PhyloTox.
Our aim is to employ existing and developing novel approaches—including Machine Learning Methods, to predict the impact in humans of the chemical compounds, based on the impact that they have on the transcriptomes of the model species (in particular by predicting the impact on co-expression networks).
This project fits overall within a larger overarching goal in our group of developing methods to relate genome to phenome across the tree of life.
About the lab The overarching theme of the research in our lab is the understanding of the information encoded in genomic sequences and how this information is processed in the pathway leading from DNA to protein sequences.
More specifically, we are interested in the epigenetic regulation of gene expression and RNA processing, the relationship between molecular phenotypes and higher order endophenotypes and organismal phenotypes, and the identification of functional regions on the genome of all living beings.
Our group is mostly computational, and we do both large-scale data analysis and development of methods, but it has also an important experimental component.
We participate in many large scale international functional genomics projects, such as ENCODE, GTEx, BluePrint and others.
Whom would we like to hire?
Professional experience Must Have - You have a strong computational background.
- You have experience in the development and/or implementation of algorithms, and/or of computational pipelines.
Desirable but not required/ Nice to have - You have background/experience in building Statistical and/or Machine Learning (e.g.
explainable/supervised/unsupervised data integration) methods.
- You have previous experience in building and interacting with relational databases (e.g.
PostgreSQL) and APIs.
- You have experience in the analysis of large-scale omics data, specifically (single-cell/bulk) RNAseq.
Experience with QTL analysis and quality control.
Education and training - You hold a PhD in Bioinformatics or in Biology, Machine Learning, Statistics, Physics, Mathematics, Chemistry or related areas.
Languages - You have a high level of spoken and written English.
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