Job Description
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.
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