Research Fellow - Population Kidney Health

About the role

The Research Fellow / Senior Research Fellow leads research on population kidney health in Singapore. The successful candidate designs and runs integrative genomics and multi-omics analyses, combining epidemiological, clinical and molecular data to understand kidney disease, risk stratification and population health outcomes. The role suits an independent scientist who can lead collaborative projects and contribute substantially to the team's scientific output.

Key responsibilities

• Lead and perform integrative genomics and multi-omics analyses on large-scale population datasets, including GWAS, polygenic risk score 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 with clinicians, epidemiologists and biostatisticians on population kidney health research.
• Develop, implement and optimise bioinformatics pipelines for multi-omics data processing and quality control.
• Assist with manuscript preparation, including literature review, data analysis and drafting.
• Liaise with local and international collaborators and present findings at local and international scientific meetings.
• Ensure compliance with ethical, regulatory and data governance requirements.

Required profile

• 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.
• Demonstrated track record of independent research.
• Prior experience with genomics, proteomics, transcriptomics or multi-omics data integration, and/or epidemiological research; population-based cohort or biobank data experience preferred.
• Experience in chronic disease research (cardiovascular, metabolic, renal or other non-communicable diseases) is an advantage; prior kidney disease research is not required but advantageous.

Required skills

• Proficiency in R and/or Python for data analysis and bioinformatics.
• Genomics tools such as PLINK, REGENIE, SAIGE or GATK.
• Familiarity with TREs or secure data platforms such as the MOHH Health Data TRUST or Lifebit is an advantage.
• Strong scientific writing, presentation and documentation skills, and strong interpersonal and communication skills.