The Roslin Institute
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Bioinformatics Research Associate

To investigate the role of structural variation in shaping cattle breed diversity. Closing on 17 May.

The mammalian genomics group at the Roslin Institute is looking for a Bioinformatician to join our team to work on a unique trans-omics dataset to investigate the role of structural variation (SV) in shaping cattle breed diversity. The successful applicant will drive their own project to use a large dataset of novel HiFi genomics sequencing data and over 150 new ATAC-seq, RRBS and RNA-seq libraries from matching animals of diverse cattle breeds to determine the impact of common and rare SVs on chromatin and gene expression levels. As well as working on this unique and extensive dataset the researcher will benefit from being part of a wider group of bioinformaticians working on both human and livestock genomics (e.g. see recent papers: https://www.nature.com/articles/s41467-020-18550-1https://www.nature.com/articles/s41467-019-13762-6), will have the opportunity to be involved in international consortia such as the bovine pangenome consortium and have access to substantial resources including the University of Edinburgh’s high performance computing infrastructure.

The position will be on grade 7 (£33,797 – £40,322) and is available initially for 30 months. Informal enquiries can be directed to Dr James Prendergast (james.prendergast@roslin.ed.ac.uk).

Please apply with your CV and a cover letter outlining your suitability for the role addressing the criteria set out in the skills section.

Your skills and attributes for success:

·         Advanced degree (MSc/PhD) in a relevant quantitative discipline (e.g. Bioinformatics, Genetics/Genomics, Computational Biology or Data science)

·         Experience with analysing “omics” data.

·         Experience programming in a high-level scripting language (R, python)

·         Proficiency in Linux/Unix operating systems

·         Experience of planning and executing research projects and complex integrative analysis of large datasets

Futher Information

All applicants are encouraged to apply online. The application process is quick and easy to follow, and you will receive email confirmation of safe receipt of your application. Applications should be received by the closing date of 17 May 2021.

Further information and application