Job Description
Research Fellow (Proteomics for PRECISE-SG100K)
We are seeking a full-time Research Fellow to join our team at the Saw Swee Hock School of Public Health (SSHSPH), National University of Singapore (NUS). The National University of Singapore (NUS) is a leading global institution known for high-impact research, innovative education, and thought leadership. The School works closely with government agencies, healthcare institutions, and international partners to develop evidence-based solutions that inform policy and advance public health in Singapore and the region. SSHSPH provides a dynamic, interdisciplinary environment that supports collaboration, professional growth, and impactful scholarship.
The SG100K project is an unique multi-ancestry population cohort dataset of ~100,000 citizens and permanent residents living in Singapore drawn from four major prospective population cohorts. Detailed research phenotyping including health and lifestyle information and physical examination were performed at recruitment. In partnership with PRECISE, whole-genomes at 30X depth were generated for these individuals. This PRECISE-SG100K resource comprising of the research phenotype data and whole genomes has been linked to individuals electronic health records at the TRUST platform (https://trustplatform.sg/). Large-scale proteomics data have also been generated for these individuals.
We are inviting a motivated Research Fellow to join our PRECISE-SG100K collaboration to work on the dataset (https://www.npm.sg/phase-ii-precise-sg100k/), specifically on the proteomics data. The Research Fellow will drive proteomics and pQTL research within a multidisciplinary team focused on understanding protein variation, molecular mechanisms of disease, and biomarker discovery. The role involves generating, analysing, and integrating large-scale proteomics datasets with genomic and clinical data, contributing to high-impact publications and translational research outcomes.
Job scope:
• Develop research questions, study designs, and analysis plans using EMR-derived data.
• Perform quality control and develop or adapt standardisation/normalisation workflow for high-throughput analyses.
• Conduct pQTL mapping and apply statistical genetics methods (e.g., GWAS, QTL mapping, colocalisation, Mendelian randomisation).
• Work closely with biologists, clinicians, bioinformaticians, and statisticians.
• Develop documentation, codebooks, or tools to support reproducible research.
• Lead manuscript preparation, conference presentations, and grant-related deliverables.
Requirements:
• Strong experience with proteomics data or other omics data.
• Proficiency in data analysis tools (e.g., R, Python, SQL) or statistical genetics tools.
• Strong written and verbal communication skills.
• Highly organized and able to work effectively independently as well as with a team.
The following requirements will be considered as an advantage:
• Experience with large-scale cohorts or biobanks.
• Familiarity with imputation, genetic association testing and multi-omics processing and pipelines.
• Experience with cloud environments and workflow managers such as Nextflow.
• Experience working with secure research environments (e.g., TREs, data enclaves).
Applicants should send the following documents during the application:
a. Cover letter highlighting relevant experience and how they meet the selection criteria
b. Curriculum Vitae, containing names and contact details of three named referees
Qualifications
• PhD in Biostatistics, Bioinformatics, Statistical Genetics, or related fields.
More Information
Location: Kent Ridge Campus
Organization: Saw Swee Hock School of Public Health
Department : Saw Swee Hock School of Public Health
Employee Referral Eligible: No
Job requisition ID : 31060
