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University of Sheffield invites applications for a data and analytics specialist within the ARIA Accelerated Adaptation programme. The role focuses on providing core data analytics support across genomics projects, and on developing pipelines and reproducible workflows.
You will train researchers in data management and bioinformatics, assist with experimental design and study curation, and collaborate to build re-analysis workflows and dashboards.
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This post will involve providing core data and analytics support to the ARIA Accelerated Adaptation programme, delivered jointly by the University of Sheffield (NEOF, based in the Genomics Laboratory of the Ecology and Evolutionary Biology cluster in the School of Biosciences, & Research Software Engineering) and the Earlham Institute (COPO Project).
The appointee will play a pivotal role in establishing a comprehensive data support framework for research teams across the programme. Key responsibilities include co-developing and delivering training and bioinformatics support in data management, data deposition, and advanced bioinformatic workflows. You will assist partner research groups with experimental design, study curation, and data analysis-likely to include low-coverage whole-genome sequencing, genotype imputation, genome-wide association studies (GWAS), genomic prediction, selection tests, and multi-omics.
In addition to providing training and bioinformatic support, the appointee will play a pivotal role in establishing shared bioinformatic pipelines (e.g., via GitHub) and collaborating with RSE specialists to support independent verification of research findings by building re-analysis workflows, evaluating scientific claims submitted by programme research groups, and helping populate shared results dashboards.
Applicants must hold a PhD (or be close to completion / have equivalent postdoctoral work experience) in Bioinformatics, Evolutionary Genomics, Quantitative Genetics, Computational Biology, or a relevant area along with experience in bioinformatic or population genomic analyses, including processing high-throughput sequencing (NGS) datasets. Strong programming skills (e.g., Linux/Bash, R, Python) and experience using High-Performance Computing (HPC) clusters are also essential.