Job Description

About the Role
The Centre for Epidemic Response & Modelling (CERM) at NUS Saw Swee Hock School of Public Health is recruiting two Research Associates/Assistants to support a diverse portfolio of research projects at the intersection of infectious disease modelling, Bayesian inference, AI, and public health. Projects span AI-driven epidemic forecasting, transmission modelling in the ASEAN region, social network dynamics and use for large language models for public good. Successful candidates will be embedded in a multidisciplinary team working with epidemiologists, clinicians, biostatisticians, data scientists, and public health agencies.

We welcome candidates with complementary skill profiles. One position is oriented towards computational/statistical modelling; the other towards data engineering, visualisation, and software development. Both positions involve hands-on research work and will suit candidates looking to build a strong research track record before or alongside a PhD. The postholder will work with Asst. Prof. Swapnil Mishra (Deputy Director, CERM and AI for Public Health Programme) and engage an active network of collaborators spanning CERM, NUS, Imperial College London, Ashoka University, the Communicable Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global Health Network (MLGH), and wider regional and global partners.

Key Responsibilities
• Implement and test statistical and computational models for infectious disease dynamics (compartmental models, Bayesian inference, phylogenetic analysis, or machine learning pipelines depending on profile).
• Process, clean, and analyse large-scale epidemiological, genomic, and/or mobility datasets.
• Develop data visualisation tools and contribute to interactive surveillance dashboards.
• Support the preparation of manuscripts, reports, conference presentations, and workshops.
• Collaborate with internal team members and external partners across institutions and countries.
• Contribute to code documentation and reproducible research workflows.

Additional Opportunities
Exceptional candidates may have the opportunity to pursue a PhD at NUS. The group actively supports career development through conference travel, training workshops, and mentorship from senior researchers and international collaborators.

Qualifications

Research Associate (higher band): Master's degree in a relevant discipline (statistics, epidemiology, computer science, bioinformatics, computational biology, biostatistics, or related) with two years of relevant work experience.

Research Assistant (entry band): Bachelor's degree with strong analytical training; candidates with no experience are welcome to apply.

• Solid quantitative background; familiarity with Bayesian inference or machine learning is an advantage.
• Proficiency in Python and/or R; experience with probabilistic programming (Stan, PyMC, NumPyro) or deep learning frameworks (PyTorch, JAX) is desirable but not required.
• Ability to work with real-world datasets and to communicate findings clearly in writing and presentations.
• Demonstrated ability to work both independently and as part of a team.
• A publication or pre-print record (for Research Associate level) is an advantage but not essential.

Interested applicants should submit the following documents:
• A cover letter explaining your interest in the position, and relevant experience,
• A comprehensive curriculum vitae.
• Contact information for two professional references (letters may be requested).