Summary
Hannah N is a postdoctoral researcher and bioinformatician with six years' experience applying statistical genetics, machine learning and cloud-scale pipelines to translate large-scale genomics and imaging data into precision medicine insights. She develops GWAS and post-GWAS workflows (fine-mapping, colocalization, PRS, PheWAS, Mendelian randomization) for cardiac MRI phenotyping using UK Biobank exomes/WGS and imputation panels, and has built clinician-facing tools to parse longitudinal diagnostic codes. Comfortable in production environments, she has implemented PySpark pipelines on Google Cloud, contributed to Open Targets’ genetics ETL, and deployed variant-prioritisation tools via Hail. Her background spans wet-lab cardiac research to cloud-native ML, enabling her to bridge domain biology and scalable engineering in drug-target prioritisation. An attention to reproducibility and data governance (GitLab, secure patient data handling) underpins her collaborative work across institutes such as the William Harvey Research Institute, Alan Turing Institute and Broad.
6 years of coding experience
2 years of employment as a software developer
Healthcare & Technology Summer School, Bioengineering and Biomedical Engineering, 2:1, Healthcare & Technology Summer School, Bioengineering and Biomedical Engineering, 2:1 at King's College London
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Queen Mary University of London
BSc (Hons) Medical Science with Professional Training Year, First Class, BSc (Hons) Medical Science with Professional Training Year, First Class at University of Exeter
English, Portuguese