Job Summary
The Research fellow / Senior Research fellow will play a key role in leading research on population kidney health in Singapore. The successful candidate will independently design and execute integrative genomics and multi-omics analyses, integrating epidemiological, clinical, and molecular data to generate mechanistic insights into kidney disease, risk stratification, and population health outcomes. This role requires a scientist who can drive research independently, lead collaborative projects, and contribute substantively to the team's scientific output.
Main Duties And Responsibilities
- Lead and perform integrative genomics and multi-omics analyses on large-scale population datasets, including genome-wide association studies (GWAS), polygenic risk score (PRS) development, Mendelian randomisation, and integration of transcriptomic, proteomic, and metabolomic data.
- Independently formulate and address scientific questions on the aetiology, diagnosis, prognosis, and prevention of kidney disease and related non-communicable diseases.
- Collaborate strategically with clinicians, epidemiologists, and biostatisticians to drive research on population kidney health.
- Develop, implement, and optimise bioinformatics pipelines for multi-omics data processing, quality control, and integration.
- Assist with manuscript preparation for submission to peer reviewed journals through literature review, data analysis and drafting of manuscripts
- Liaise with local and international collaborators to integrate clinical and scientific insights into new and ongoing research projects
- Present research findings at local and international scientific meetings and conferences
- Ensure compliance with ethical, regulatory and data governance requirements
Job Requirements
- EDUCATION AND TRAINING
- PhD in Bioinformatics, Computational Biology, Epidemiology, Biomedical Science, Public Health, or a related field (preferred). Master's degree holders with relevant independent research experience will be considered.
- Strong background in statistical and computational analysis of large population or biobank datasets
- TRAINING / SKILLS
- Proficiency in programming languages for data analysis and bioinformatics (R and/or Python).
- Proficiency in genomics tools, such as PLINK, REGENIE, SAIGE, or GATK.
- Familiarity with trusted research environments (TREs) or secure data platforms such as the MOHH Health Data TRUST or Lifebit will be an advantage.
- Strong scientific writing, presentation and documentation skills
- Strong interpersonal and communication skills to liaise with investigators, research coordinators and administrative teams
- Demonstrated ability to work both independently and collaboratively across disciplines
- EXPERIENCE
- Demonstrated track record of independent research
- Prior experience with genomics, proteomics, transcriptomics, or multi-omics data integration and/or epidemiological research.
- Experience with population-based cohort studies or biobank data is preferred.
- Prior experience in chronic disease research (e.g., cardiovascular, metabolic, renal, or other non-communicable diseases) will be considered an advantage. Prior kidney disease research experience is not required but will also be advantageous.
