Research Assistant or Postdoctoral Research Associate in Bioinformatics and Machine Learning
Research Assistant or Postdoctoral Research Associate in Bioinformatics and Machine Learning
Queen Mary University of London - School of Biological and Behavioural Sciences
| Location: | London |
| Salary: | £38,419 to £49,130 per annum |
| Hours: | Full Time |
| Contract Type: | Fixed-Term/Contract |
Placed On: 2nd February 2026
Closes: 15th February 2026
Job Ref: 8877
About the Role
We are looking for an enthusiastic data scientist with biology knowledge and machine learning expertise to help us understand the role of microbial and inflammatory exposures during pregnancy on preterm birth. Using a large in-house dataset including microbiome profiles and inflammatory biomarkers from multiple body-sites during pregnancy in addition to clinical factors and associated data from a recently completed clinical trial (https://www.nejm.org/doi/full/10.1056/NEJMoa2408114), you will build and evaluate multimodal machine learning models that integrate these data to predict the likelihood of preterm births. Successful models will be investigated to help elucidate mechanisms driving preterm births.
You will join a team of established research experts in data science (Prof Conrad Bessant), microbiome science (Dr Ruairi Robertson), and paediatric infection and immunology (Prof Andrew Prendergast). This MRC-funded role is for 12 months in the first instance, and is expected to result in at least one high impact journal article.
About You
You will have expertise in data science (especially machine learning), knowledge of core biomedical concepts, and a demonstrable interest in bringing these things together to answer research questions. The ideal candidate will have a PhD in a relevant data-centric field, but high-ranking graduates from relevant interdisciplinary MSc programmes (e.g. bioinformatics or systems biology) will also be considered.
About the School/Department/Institute/Project
Organisationally, the role sits within the School of Biological and Behavioural Sciences, one of the largest Schools within Queen Mary University of London. The school has a strong research output and demanding educational standards for its students, but all members benefit from a high level of academic and social integration. In addition, the school is committed to equality of opportunity and holds a Silver Athena SWAN Award.
You will be based in the Digital Environment Research Institute – QMUL’s centre for applied AI and data science research – where you will benefit from working among experts in machine learning and bioinformatics. You will also spend significant time at the Blizard Institute, which integrates basic science, clinical work and translational research to link lab discoveries with patient care.
About Queen Mary
At Queen Mary University of London, we believe that a diversity of ideas helps us achieve the previously unthinkable.
Throughout our history, we’ve fostered social justice and improved lives through academic excellence. And we continue to live and breathe this spirit today, not because it’s simply ‘the right thing to do’ but for what it helps us achieve and the intellectual brilliance it delivers.
We continue to embrace diversity of thought and opinion in everything we do, in the belief that when views collide, disciplines interact, and perspectives intersect, truly original thought takes form.
Benefits
We offer competitive salaries, access to a generous pension scheme, 30 days’ leave per annum (pro-rata for part-time/fixed-term), a season ticket loan scheme and access to a comprehensive range of personal and professional development opportunities. In addition, we offer a range of work life balance and family friendly, inclusive employment policies, flexible working arrangements, and campus facilities.
Queen Mary’s commitment to our diverse and inclusive community is embedded in our appointments processes. Reasonable adjustments will be made at each stage of the recruitment process for any candidate with a disability. We are open to considering applications from candidates wishing to work flexibly.
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