Research Associate

We are seeking an enthusiastic, dynamic, and ambitious postdoctoral Research Associate to join the group of Dr Nicholas Gleadall within the University of Cambridge Department of Haematology, based at the Victor Phillip Dahdaleh Heart and Lung Research Institute on the Cambridge Biomedical Campus.
The postholder will work as part of the BloodCounts! consortium, an international collaboration applying artificial intelligence and machine learning to complete blood count (CBC) data for the detection of disease. The consortium brings together clinical and academic partners across the UK, Europe, Singapore, and United States, and has significant funding from the Gates Foundation and other major funders.
The postholder will lead computational analyses to evaluate machine learning methodologies developed by the Higgins group at Massachusetts General Hospital (MGH), using ethnically diverse CBC data assembled by the BloodCounts! consortium from Cambridge University Hospitals, Barts Health NHS Trust, University College London Hospitals, and Amsterdam UMC¿totalling approximately 9.6 million CBC records. The overarching aim is to evaluate whether patient-specific CBC baselines, which have been shown to provide superior diagnostic accuracy compared to population reference intervals, generalise across diverse populations and alternative CBC testing platforms. A key focus will be the application of these approaches to anaemia classification and subtyping in pregnancy., Implement and validate MGH algorithms on large-scale CBC datasets from Cambridge University Hospitals, Barts Health, UCLH, and Amsterdam UMC.
Conduct statistical and computational analyses to evaluate methods across diverse patient populations, with particular attention to variation by ethnicity and clinical context.
Analyse longitudinal CBC data from a pregnancy sub-cohort of approximately 23,500 pregnancies to assess how physiological changes during pregnancy affect haematological setpoints and population dynamics.
Estimate pre-pregnancy setpoints using data from large healthy population cohorts (INTERVAL, COMPARE, STRIDES) and evaluate their diagnostic utility compared to booking blood values.
Explore the portability of setpoint methodologies across CBC testing platforms, comparing Siemens ADVIA and Sysmex instrument data to determine whether patient-specific baselines can be reliably identified on both platforms.
Help manage, curate, and harmonise heterogeneous CBC and electronic health record datasets received from multiple consortium partner sites, ensuring data integrity and compliance with information governance requirements.
Write up research findings for publication in peer-reviewed journals and present results at national and international conferences.
Contribute to the preparation of grant applications, reports for funding bodies, and consortium documentation.
Liaise with consortium partners across the UK, Europe, Singapore, and the United States to coordinate data sharing and collaborative analyses.

You will have a PhD in a relevant quantitative discipline¿such as computational biology, data science, statistics, or bioinformatics. You will demostrate experience in statistical and computational analysis of large-scale clinical or biomedical datasets, ideally including electronic health records or haematology data. Proficiency in Python is essential. Experience with machine learning methods applied to clinical data, longitudinal data analysis, or multi-site federated research environments would be advantageous. You will have strong communication and organisational skills and the ability to work both independently and as part of a large international team. The postholder will be expected to undertake short-term research visits to MGH during the appointment.
Informal enquiries to Lindsay Walker:
Funds for this post are available for 2-years in the first instance.