13034 - Bioinformatician
| Posting date: | 03 September 2025 |
|---|---|
| Salary: | £41,064 to £48,822 per year |
| Hours: | Full time |
| Closing date: | 24 September 2025 |
| Location: | Edinburgh, Scotland |
| Remote working: | On-site only |
| Company: | University of Edinburgh |
| Job type: | Contract |
| Job reference: | 13034 |
Summary
Grade UE07: £41,064 - £48,822 per annum (pro rata if part time)
Clinical Sciences / Institute for Regeneration and Repair / Centre for Inflammation Research
Full-time: 35 hours per week
Fixed Term: 3 years
We are looking for a Bioinformatician to study cellular responses to injury and ageing using integrated single-cell, spatial and proteomic technologies. The post will be based in the Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh with Professor David Ferenbach.
This post is advertised as 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.
The Opportunity:
This fellowship forms part of an MRC-funded Senior Clinical Fellowship, based in the Institute for Regeneration and Repair – home to over 1000 research staff and students. Its focus is on the identification of novel pathways linking senescent epithelia to tissue fibrosis in human chronic kidney disease. The post holder will lead the computational identification and analysis of these pathways using spatial transcriptomic analysis of human kidney biopsies, and single-cell resolution analysis of renal cell populations obtained ex vivo / in vitro. Further work will explore the potential for these pathways to be targeted in vivo to promote senescent cell clearance and prevent senescent cell accumulation with ageing and tissue injury.
Your skills and attributes for success:
A PhD, or near completion, in Mathematics, Physics, Computational Science, Genomics, Genetics, Computational Biology (or similar), and a passion for problem solving.
Understanding of single cell transcriptome and/or genome data analysis.
Proficiency in analysing NGS sequencing data and (RNA-seq, ATAC-seq, Chip-seq, single cell)
An understanding, experience and published outcomes from analysing and interpreting large datasets using at least one statistical package (e.g. R/SPLUS, SAS) and proficiency with scripting languages (R/Python) and Linux/Bash
Experience in the analysis of high-throughput biological datasets.
Clinical Sciences / Institute for Regeneration and Repair / Centre for Inflammation Research
Full-time: 35 hours per week
Fixed Term: 3 years
We are looking for a Bioinformatician to study cellular responses to injury and ageing using integrated single-cell, spatial and proteomic technologies. The post will be based in the Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh with Professor David Ferenbach.
This post is advertised as 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.
The Opportunity:
This fellowship forms part of an MRC-funded Senior Clinical Fellowship, based in the Institute for Regeneration and Repair – home to over 1000 research staff and students. Its focus is on the identification of novel pathways linking senescent epithelia to tissue fibrosis in human chronic kidney disease. The post holder will lead the computational identification and analysis of these pathways using spatial transcriptomic analysis of human kidney biopsies, and single-cell resolution analysis of renal cell populations obtained ex vivo / in vitro. Further work will explore the potential for these pathways to be targeted in vivo to promote senescent cell clearance and prevent senescent cell accumulation with ageing and tissue injury.
Your skills and attributes for success:
A PhD, or near completion, in Mathematics, Physics, Computational Science, Genomics, Genetics, Computational Biology (or similar), and a passion for problem solving.
Understanding of single cell transcriptome and/or genome data analysis.
Proficiency in analysing NGS sequencing data and (RNA-seq, ATAC-seq, Chip-seq, single cell)
An understanding, experience and published outcomes from analysing and interpreting large datasets using at least one statistical package (e.g. R/SPLUS, SAS) and proficiency with scripting languages (R/Python) and Linux/Bash
Experience in the analysis of high-throughput biological datasets.