Postdoctoral Researcher – Genomics, Proteomics, and Clinical Outcomes

Role Summary

Postdoctoral Researcher – Genomics, Proteomics, and Clinical Outcomes. The role focuses on integrating and analyzing genomic, proteomic, metabolomic and phenotypic data from biobank and cohort datasets to advance precision medicine and therapeutic target identification within the DOCTA Data Science and Computational Biology team.

Responsibilities

• Apply statistical and computational approaches to analyze WGS/WES, proteomics, metabolomics, and clinical data for biomarker discovery.
• Conduct analyses of large-scale population cohorts and biobank datasets to identify genetic variants and causal genes associated with disease outcomes.
• Develop and implement machine learning and bioinformatics pipelines to integrate multi-omics data.
• Collaborate with interdisciplinary teams to interpret findings and guide therapeutic development.
• Prepare scientific reports, presentations, and publications detailing research outcomes.
• Contribute to the development of novel statistical methods for analyzing high-dimensional biological data.

Qualifications

• PhD in statistical genetics, bioinformatics, computational biology, biostatistics, or a related quantitative field

Preferred Qualifications

• Expertise in whole genome and whole exome sequencing analysis, proteomics, metabolomics and other molecular data analysis, and clinical outcomes research.
• Strong proficiency in statistical modeling, machine learning, and high-dimensional data analysis.
• Experience with large biobank and cohort datasets (e.g., UK Biobank, All of Us, FinnGen).
• Proficiency in R, Python, and SQL for data analysis.
• Familiarity with genetic association studies, GWAS, and polygenic risk scores.
• Excellent communication and collaboration skills for cross-functional teamwork.
• Experience in pharmaceutical or biotech industry settings.
• Knowledge of functional genomics and multi-omics data integration.
• Strong publication record in statistical genetics and biomarker discovery.
• Prior experience in cardiometabolic research.
• Prior experience with polygenic risk score models.