Postdoctoral Researcher – Integrative Genetics and Multi-Omics Data Science

Role: Postdoctoral researcher in the Data, AI and Genome Sciences department focusing on developing computational and statistical methods for large-scale biobank data integration and biomarker discovery. Objectives include uncovering causal gene-disease relationships, identifying biomarkers, and supporting translational research across therapeutic areas. Responsibilities: design pipelines combining genetics, proteomics, EHR, and other datasets; develop causal modeling frameworks integrating human genetics/genomics and perturbation data; analyze biobank-scale omics datasets; stay abreast of cutting-edge methods in statistical genetics and computational biology; collaborate with wet-lab and computational teams; disseminate findings via publications and presentations. Requirements: PhD in Statistics, Biostatistics, Statistical Genetics, Genetic Epidemiology, Computational Biology, Bioinformatics, or related field (or completion by Spring 2027); experience working with large-scale datasets such as UK Biobank or All of Us; proficiency in analyzing omics data (WGS, genotyping, transcriptomics, proteomics); hands-on in statistical genetics methods like GWAS and QTL mapping; programming expertise in R and/or Python; familiarity with cloud or HPC environments; strong communication skills. Preferred: experience in biomarker discovery and computational biology. High-Value: biobank-scale data analysis, multi-omics integration, causal modeling, cross-functional collaboration in a research setting. Location: unspecified; travel requirement approximately 10%.