Vacancies

Join the University of Edinburgh and you’ll be making a difference to everything around you. Be part of something bigger — where you’ll do meaningful work, grow and progress, be rewarded and recognised, and benefit from our strong commitment to your wellbeing. There are so many reasons to join us. 

The Opportunity:

We are looking for an enthusiastic postdoctoral Research Fellow to join the Rajani Lab as part of the new national BHF-UK DRI Centre for Vascular Dementia Research. We use a range of innovative methodologies and models to understand oligodendrocyte changes in vascular and Alzheimer’s dementias, and the interaction of these with other cell types. In this project, funded as part of the UK DRI’s Key Questions programme, the Research Fellow will use iPSC-derived oligodendrocytes to understand the role of genes identified as being epigenetically altered in Alzheimer’s disease and ageing. The Research Fellow will further investigate how these affect oligodendrocyte interactions with neurons and microglia.

Your skills and attributes for success:

• A PhD (or close to completion) in neuroscience or other relevant discipline.
• Experience with glial and/or neuronal cell cultures.
• Ability to work independently.
• Proactive and creative independent thinker.
• Ability to problem-solve and troubleshoot technical difficulties.

This post is full-time (35 hours per week) and 100% on campus.

Informal enquiries are welcomed and can be addressed to Dr Rikesh Rajani (rikesh.rajani@ed.ac.uk) 

About us

The UK DRI Core Informatics team serves as the core for the UK DRI national informatics programme. The team enhances data access, training, partnerships, and technical capacity for researchers, fostering data sharing and boosting data science capabilities across the national institute. The team is currently based at UCL and interacts closely with the UK DRI at UCL centre staff.

About the role

We are excited to welcome an enthusiastic and experienced Software Engineer to our team. You will lead the ongoing development and maintenance of DataMap, a data-driven research software platform designed to extract knowledge from biological datasets and incorporate it into a knowledge graph. You will work across backend services, frontend applications, graph data models, databases, observability, and Kubernetes to ensure the platform remains robust, scalable, usable, and aligned with research needs.

The role requires someone who can operate as both a strong hands-on engineer and a technical lead: translating requirements from UK DRI researchers into production-quality software, improving architecture and engineering practices, and supporting long-term sustainability of the platform.

The post is available immediately and is funded by the UK DRI until 31 March 2028 in the first instance.

This role is eligible for hybrid working with a minimum of 20% of time on site.

For informal enquiries about the role please contact Dr Amonida Zadissa (amonida.zadissa@ukdri.ac.uk ).

About you

You should have professional software engineering experience with a focus on data science or data-intensive applications, in-depth experience designing and developing APIs and backend services, and hands-on experience in web UI development using React and TypeScript. Strong hands-on experience with Python web frameworks, proven experience working with graph databases (especially Neo4j) and a working knowledge of cloud or cloud-native systems (including AWS, Azure, or Kubernetes) is essential. A good understanding of production software systems across development, deployment, and maintenance lifecycles, and the ability to take ownership of a complex technical platform and drive work independently, are also needed for this role.

This role meets the eligibility requirements for a skilled worker certificate of sponsorship or a global talent visa under UK Visas and Immigration legislation. Therefore, UCL welcomes applications from international applicants who require a visa.

About the role

We are looking for a motivated Research Associate to lead the genomic and proteomic analyses for STRAT-GLP1, a research programme funded by The Michael J. Fox Foundation for Parkinson’s Research (MJFF). This post will deliver the core analytical aims of the project, using human genetics, multi-cohort genome-wide association studies (GWAS), plasma proteomics, and drug-target Mendelian randomisation to define metabolically stratified Parkinson’s disease (PD) subtypes and identify which patients are most likely to respond to GLP-1 receptor agonists (GLP-1RAs).

What you would be doing

You will be working closely with Dr Cynthia Sandor and collaborators at Cardiff University (Prof Caleb Webber, Dr Samuel Neaves, and Dr Viola Volpato). The project aims to understand how metabolic factors — including insulin resistance, glycaemic dysregulation, adiposity, lipid metabolism, and renin–angiotensin system activity — influence PD risk, genetic architecture, and disease progression. Using large international datasets including UK Biobank, All of Us, Our Future Health, PPMI, and OPDC/Tracking, you will perform stratified GWAS and drug-target Mendelian randomisation to identify genetically defined PD subtypes, and translate these findings into clinically actionable blood-based proteomic biomarkers using Olink (PPMI and UK Biobank) and SomaScan (OPDC/Tracking) platforms.

You will be the primary analyst on the project, with access to established data pipelines and secure compute environments from day one (UK Biobank Application 69610; PPMI; All of Us; Our Future Health; OPDC/Tracking). Advanced statistical approaches will be used to integrate genetic and proteomic data to identify patterns of disease progression and potential therapeutic targets, including GLP1R-related pathways.

What we are looking for

You will hold a PhD in statistical genetics, computational biology, bioinformatics, epidemiology, or a closely related quantitative discipline. You should have demonstrable experience in GWAS, Mendelian randomisation, and large-scale biobank data analysis. Familiarity with proteomic datsets and Bayesian modelling is desirable but not essential.