Research Fellow

Institution: Tan Tock Seng Hospital

Family Group: Allied Health

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

(A) 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

(B) 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

(C) 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.