Postdoctoral Researcher - Human Genetics, Proteomics, and Multi-Omics for Drug Target Discovery

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Please refer to the How to Apply for a Job (for External Candidates) job aid for instructions on how to apply. If you are an active McGill employee (ie: currently in an active contract or position at McGill University), do not apply through this Career Site. Login to your McGill Workday account and apply to this posting using the Find Jobs report (type Find Jobs in the search bar). The Yoshiji Lab at McGill University is recruiting a fully funded Postdoctoral Fellow to lead projects at the intersection of human genetics, proteomics, and multi-omics for drug target discovery and precision medicine in cardiometabolic diseases and beyond. The successful candidate will analyze biobank-scale datasets - including UK Biobank (~500,000 participants), NIH All of Us (~440,000), the Canadian Longitudinal Study on Aging (CLSA, ~26,000), and the McGill-based BioPortal, which the Yoshiji Lab co-leads (~3,000 participants with ~5,400 plasma protein measurements) - to identify and characterize causal proteins and genetic variants that can be translated into therapeutic targets. The Fellow will join a vibrant and growing team (5 PhD students, 1 MSc student, 1 visiting professor, and 1 visiting graduate trainee) and contribute to active international collaborations, including with the Broad Institute / Harvard, UCSD, Stanford, Kyoto University, Uppsala University, and the Type 2 Diabetes Global Genetics Consortium. Representative lab work includes the lab's recent Nature Genetics publication on proteome-wide Mendelian randomization and proteome-phenome mapping Primary Responsibilities The Postdoctoral Fellow will be expected to: Design and execute independent research projects applying Mendelian randomization, GWAS, fine-mapping, colocalization, polygenic scoring, and multi-trait / multi-ancestry methods to biobank-scale human genetics and proteomics data. Analyze plasma and tissue proteomics data (e.g., Olink, SomaScan) integrated with genotype, phenotype, and electronic health record data across UK Biobank, All of Us, CLSA, and the McGill BioPortal. Develop and maintain reproducible analysis pipelines in R, Python, and shell on HPC / Slurm clusters and cloud environments (e.g., DNAnexus, Terra, AWS/GCP). Write first-author manuscripts for peer-reviewed journals and present at international conferences. Prepare and contribute to grant and fellowship applications (e.g., CIHR, FRQS, JSPS, HFSP). Collaborate with internal team members and external consortia, including joint analyses and co-authored publications. Contribute to lab life: mentoring graduate and undergraduate trainees, participating in lab meetings and journal clubs, and supporting shared infrastructure (code review, documentation, data governance). Uphold the highest standards of research integrity, reproducibility, and responsible use of human biobank data (ethics approvals, data access agreements, privacy). Required Qualifications Candidates must hold, or be within ~6 months of completing, a PhD (or MD/PhD) in a relevant field - for example human genetics, statistical genetics, computational biology, bioinformatics, epidemiology, biostatistics, computer science, or a closely related quantitative discipline. In addition, the successful candidate will demonstrate: Programming proficiency in at least R or Python (ideally both), plus comfortable use of Unix/Linux shell. Hands-on experience with high-performance computing (Slurm/PBS or equivalent) and/or cloud computing platforms. Working knowledge of at least one of: GWAS, Mendelian randomization, fine-mapping, colocalization, polygenic risk scores, or related statistical-genetics / causal-inference methods. Strong publication record appropriate to career stage, including at least one first-author peer-reviewed publication (or accepted preprint) in a quantitative biomedical area. Scientific writing and communication skills in English, including evidence of independent manuscript or thesis writing. Ability to work both independently and collaboratively in an international, interdisciplinary team. Commitment to open, reproducible science (version control with Git, documented pipelines, data-sharing awareness). Preferred Qualifications (Assets) The following are considered strong assets but are not required: Experience with biobank-scale data (UK Biobank, All of Us, FinnGen, CLSA, BBJ, Million Veteran Program, or similar). Experience with plasma or tissue proteomics platforms (Olink, SomaScan, mass spectrometry). Experience with single-cell or spatial transcriptomics, or other multi-omics modalities. Familiarity with drug target discovery, target validation, or pharmacoepidemiology. Experience contributing to international consortia or leading multi-site analyses. Bilingualism in English and French (asset in Montreal, not required). Prior success obtaining competitive postdoctoral fellowships (CIHR, FRQS, Banting, HFSP, EMBO, JSPS, Marie Skłodowska-Curie, etc.).

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