Bioinformatics Research Fellow

Full time: 35 hours per week

Fixed-term: 1^st September 2027 

We are seeking a candidate to join our team to generate the first large-scale single-cell transcriptomic atlas of African cattle. The project has generated high coverage single-cell RNA-seq data from 200 Bos indicus animals, covering all major peripheral immune-cell types alongside matched whole-genome sequence data. As the lead computational scientist you will convert these data into biological insights, developing and applying state-of-the-art methods to pinpoint the DNA variants that regulate immune-cell function and disease resistance. As part of this, the post-holder will work closely with partners in both the UK and Kenya.

Your skills and attributes for success:

• Design, implement and maintain scalable pipelines for processing single-cell RNA-seq, mapping expression quantitative trait loci (eQTL) and annotating regulatory variants.
• Train, evaluate and refine AI/ML models that integrate sequence, epigenomic and comparative-genomic features to predict causal variants across immune-cell types.
• Exploit the uniquely deep single-cell dataset, together with existing ATAC-seq, PRO-Cap and MPRA resources, to compile an openly shared catalogue of validated immune-regulatory variants.
• Collaborate with wet-lab and field partners in the UK and Kenya on experimental validation (e.g. CRISPR, MPRA) and translation into breeding or editing strategies.
• Disseminate code, models and findings through open-access repositories, peer-reviewed publications and international conferences.
• Contribute to student supervision, project reporting and the vibrant computational biology community at Edinburgh.

Informal enquiries can be directed to Prof James Prendergast (james.prendergast@roslin.ed.ac.uk). 

Essential skills:

• PhD (or PhD nearing completion) or equivalent experience in a relevant quantitative discipline (e.g. Bioinformatics, Computer science, Genetics/Genomics, Computational Biology or Data science).
• Demonstrated experience with NGS data analysis—ideally scRNA-seq or eQTL—using R/Python, Linux and Git.
• Experiencing with workflow languages e.g. Nextflow, Snakemake.
• Proficiency in Linux/Unix operating systems.
• Experience of planning and executing research projects and complex integrative analysis of large datasets.

Desired skills:

• Machine learning experience in R or Python is desirable.
• Experience with running analyses in an HPC environment.

What we offer:

• Access to unique datasets including one of the largest single-cell RNA datasets ever generated in livestock, plus world-class HPC facilities.
• Funding for training, conference travel and at least one research visit to collaborators in Nairobi.

Advert information