Research Assistant (Quantitative)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 40,000 - 60,000; Full time

Job summary

A renowned educational institution in Singapore is seeking a Research Assistant with a quantitative background to contribute to ongoing research in Public Health. The successful candidate will use statistical modelling and programming skills, working alongside a team in infectious disease dynamic modelling and data analysis. The role emphasizes independence, academic creativity, and offers support for pursuing research publications. Candidates with an MSc and significant public health research experience are encouraged to apply.

Qualifications

• MSc in statistics, mathematics, computational biology, or data science.
• Experience in public health research and leading modelling projects.
• One or more academic publications in infectious disease modelling.

Responsibilities

• Conduct infectious disease modelling.
• Perform statistical analyses.
• Engage in academic writing and publication.

Skills

Statistical modelling

Fluent in R programming

Coding knowledge in C++, Python, or Julia

Independent and organized

Education

MSc in a quantitative discipline

Job description

Interested applicants are invited to apply directly at the NUS Career Portal. Applications will be processed only if submitted through the portal. We regret that only shortlisted candidates will be notified.

Job Description

Applications are invited for the following full‑time position in the Saw Swee Hock School of Public Health: Research Assistant.

We are looking for research assistants with a quantitative background for ongoing research in Public Health.

They will be working within the team under the Principal Investigator Assistant Professor Akira Endo alongside multiple collaborators and experts.

Methods include renewal process, network transmission modelling, branching process, Bayesian inference, particularly in the context of epidemiology and dynamics of respiratory and/or sexually‑transmitted infections.

Candidates need to be able to understand infectious disease dynamic modelling, statistical modelling, have a sufficient epidemiological, mathematical and data science background, and be fluent in R programming. We will also appreciate candidates who have extensive C++, Python or Julia coding knowledge.

The candidate will be working with the Principal Investigator(s) on the analysis of large‑scale behaviour and disease data, building up mathematical models of disease transmission including network science or branching process approaches.

The Principal Investigator(s) is seeking an independent worker who is well‑organized, analytical and codes competently. They will however receive support from a team of mathematicians, epidemiologists and statisticians, and have a diverse portfolio of tasks. Under the team’s guidance, they will be expected to co‑lead their own publications.

We welcome academic creativity and will be highly supportive of candidates who wish to pursue academia or a career progression provided they show self‑motivation to showcase their problem‑solving abilities.

Responsibilities

• Infectious disease modelling
• Statistical analyses
• Stochastic processes
• Academic writing and publication of results
• Preparation of meeting materials for stakeholders

Requirements

• Completed an MSc in a quantitative discipline (statistics, mathematics, computational biology, data science).
• Extensive experience in public health research and experience in leading at least one infectious disease modelling project.
• At least one (preferably more) academic publication as a main author (first/corresponding/last) in disciplines relevant to infectious disease modelling.
• Strong programming skills (at least one of R, Python, or Julia).
• Statistical competence (understands and can perform likelihood‑based inference, ideally including Bayesian).