Postdoctoral Research Associate in Mathematical and Statistical Methods for Genome Privacy

This postdoctoral position at the University of Cambridge focuses on advancing mathematical and statistical techniques to ensure privacy in human pangenome references. The successful candidate will collaborate with Dr Gamze Gursoy’s research group in the Department of Applied Mathematics and Theoretical Physics, contributing to cutting-edge work in differentially private graph analysis, privacy-aware algorithms, and optimization for genomic data. The role emphasizes interdisciplinary collaboration, particularly with Professor Po-Ling Loh’s team in statistical genetics, and offers significant autonomy to shape research directions. Ideal candidates will have a strong quantitative background in statistics, graph theory, or computational biology, with a passion for applying privacy-preserving methods to genomics.

• Develop new mathematical and computational methods for privacy-preserving human pangenome references, including differentially private graph construction and analysis.
• Design and implement privacy-aware graph algorithms and representations tailored for large-scale genomic data.
• Conduct statistical characterization of privacy-utility trade-offs in genomic datasets.
• Optimize algorithms for large genomic graphs and benchmark methods on human pangenome data.
• Collaborate with Professor Po-Ling Loh and other researchers in statistical genetics and privacy-preserving computation.
• Manage independent research projects with guidance from the principal investigator, contributing to the intellectual life of the research group.
• Present research findings through publications, seminars, and conference presentations.