Research Fellow (Health Informatics for PRECISE-SG100K)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

A leading educational institution in Singapore is looking for a full-time Research Fellow in Health Informatics. This role focuses on working with the PRECISE-SG100K dataset, applying EMR data to advance precision medicine. Key responsibilities include developing research questions, analyzing health informatics data, and collaborating with multidisciplinary teams. The ideal candidate holds a PhD in a relevant field and has strong experience in handling large clinical datasets. This opportunity supports impactful health research and professional growth.

Qualifications

• PhD in related fields is mandatory.
• Experience with statistical analyses or machine learning.
• Ability to extract, manage, and analyze EMR data.

Responsibilities

• Develop research questions and analysis plans using EMR data.
• Extract and curate EMR data ensuring high-quality datasets.
• Collaborate with clinicians on interpreting clinical variables.
• Conduct statistical analyses and machine learning as needed.
• Prepare manuscripts and presentations.

Skills

Experience with EMR or large clinical datasets

Proficiency in data analysis tools (R, Python, SQL)

Strong written and verbal communication skills

High organization and independent work effectiveness

Education

PhD in Health Informatics, Public Health, Biostatistics, Computer Science, Biomedical Informatics, Epidemiology

Tools

R

Python

SQL

Job description

Interested applicants are invited to apply directly at the NUS Career Portal.

Your application will be processed only if you apply via NUS Career Portal.

We regret that only shortlisted candidates will be notified.

Job Description

Research Fellow (Health Informatics 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 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 the research phenotype data and whole genomes has been linked to individuals' electronic health records at the TRUST platform (https://trustplatform.sg/).

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 health informatics data (electronic medical records). The Research Fellow will lead and support research projects that utilise electronic medical records to generate insights that can advance precision medicine and population health from the PRECISE‑SG100K dataset. The role involves extracting, managing, analysing and interpreting EMRs‑derived data, and working closely with clinicians, data engineers in multi‑disciplinary research teams.

Job Scope

• Develop research questions, study designs, and analysis plans using EMR‑derived data.
• Extract, clean, and curate EMR data to ensure high‑quality curated datasets through rigorous data validation and harmonisation.
• Collaborate closely with clinicians to interpret clinical variables, coding systems (ICD/SNOMED), and workflow context.
• Conduct statistical analyses, longitudinal modelling, or machine learning approaches as appropriate.
• Develop documentation, codebooks, or tools to support reproducible research.
• Lead manuscript preparation, conference presentations, and grant‑related deliverables.

Requirements

• Strong experience with EMR or other large clinical datasets.
• Proficiency in data analysis tools (e.g., R, Python, SQL).
• Strong written and verbal communication skills.
• Highly organised and able to work effectively independently as well as with a team.

Preferred Qualifications

• Experience with health data standards (ICD, CPT, LOINC, SNOMED CT).
• Familiarity with hospital workflows or clinical terminology.
• Experience with machine learning, natural language processing, or predictive modelling.
• Experience working with secure research environments (e.g., TREs, data enclaves).

Applicants should send the following documents during the application:

1. a. Cover letter highlighting relevant experience and how they meet the selection criteria
2. b. Curriculum Vitae, containing names and contact details of three named referees

Qualifications

• PhD in Health Informatics, Public Health, Biostatistics, Computer Science, Biomedical Informatics, Epidemiology, or related fields.