12788 - Bioinformatician for Bacterial Genome Data Analysis
| Posting date: | 17 July 2025 |
|---|---|
| Salary: | £40,497 to £48,149 per year |
| Hours: | Full time |
| Closing date: | 31 July 2025 |
| Location: | EH25 9RG |
| Remote working: | Hybrid - work remotely up to 3 days per week |
| Company: | University of Edinburgh |
| Job type: | Temporary |
| Job reference: | 12788 |
Summary
Grade UE07: £40,497 - £48,149 per annum (pro rata if part-time)
CMVM / Royal (Dick) School of Veterinary Studies / The Roslin Institute
Full-time: 35 hours per week
Fixed-term: 31st March 2026
The Opportunity:
We are seeking a bioinformatics scientist to join our research group at the Roslin Institute and help analyse genome sequences from a large collection of E. coli strains carrying Shiga toxin genes. The candidate will be applying their bioinformatics expertise to a range of projects, including phylogenetics, statistical analyses, and machine learning approaches that link genotype, primarily genome sequence data, to phenotype. A key focus will be developing models to predict the potential threat of various strains to human health.
The successful applicant will collaborate closely with another bioinformatician who has been building machine learning pipelines for source attribution. This post will further develop and apply these tools to our sequenced STEC dataset. The role will also involve setting up a server for the group and contributing to other machine learning projects, particularly those related to predicting bacteriophage activity.
Although the initial funding is until 31st March 2026, additional grants and pending applications may allow for an extension of the contract.
This post is full-time (35 hours per week); however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
Your skills and attributes for success:
Degree in a biological science, maths or computing subject area and a Ph.D obtained or submitted (awaiting viva) in a relevant biological science with bioinformatic component.
Experience working with high performance computing (HPC) with large data sets is essential.
Experience with pipelines for genomic sequence analysis from raw reads.
Relevant experience with Python/R.
Evidence of manuscript and/or report writing.
CMVM / Royal (Dick) School of Veterinary Studies / The Roslin Institute
Full-time: 35 hours per week
Fixed-term: 31st March 2026
The Opportunity:
We are seeking a bioinformatics scientist to join our research group at the Roslin Institute and help analyse genome sequences from a large collection of E. coli strains carrying Shiga toxin genes. The candidate will be applying their bioinformatics expertise to a range of projects, including phylogenetics, statistical analyses, and machine learning approaches that link genotype, primarily genome sequence data, to phenotype. A key focus will be developing models to predict the potential threat of various strains to human health.
The successful applicant will collaborate closely with another bioinformatician who has been building machine learning pipelines for source attribution. This post will further develop and apply these tools to our sequenced STEC dataset. The role will also involve setting up a server for the group and contributing to other machine learning projects, particularly those related to predicting bacteriophage activity.
Although the initial funding is until 31st March 2026, additional grants and pending applications may allow for an extension of the contract.
This post is full-time (35 hours per week); however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
Your skills and attributes for success:
Degree in a biological science, maths or computing subject area and a Ph.D obtained or submitted (awaiting viva) in a relevant biological science with bioinformatic component.
Experience working with high performance computing (HPC) with large data sets is essential.
Experience with pipelines for genomic sequence analysis from raw reads.
Relevant experience with Python/R.
Evidence of manuscript and/or report writing.