Senior Research Fellow (Infectious Disease Modelling & AI for Public Health)

Job Description

About the Role

The Centre for Epidemic Response & Modelling (CERM) at NUS Saw Swee Hock School of Public Health invites applications for a Senior Research Fellow to help lead a vibrant, internationally connected research programme spanning Bayesian infectious disease modelling, AI-driven epidemic forecasting, genomic epidemiology, and pandemic preparedness. 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.

This is a senior scientific role with significant autonomy. The successful candidate is expected to drive independent research streams, provide intellectual leadership across multiple concurrent grants, and mentor junior researchers. The portfolio spans methodological innovation and applied public health impact, including real-time surveillance platforms, lineage transmissibility models, AIpowered decision-support tools, and equitable AI for Public Health in Asian settings.

Key Responsibilities

• Lead and independently execute high-impact research across one or more of the group's active programmes: Bayesian genomic-epidemiological modelling, AI for epidemic forecasting, and arbovirus genomic surveillance.
• Design novel statistical and computational methodologies and publish them in leading journals and present at international conferences.
• Provide scientific leadership and day-to-day mentorship to Research Fellows, Research Associates, and Research Assistants.
• Take a leading role in grant writing, progress reporting, and engagement with funding agencies and public health partners.
• Represent the group at national and international conferences; build and sustain collaborative networks.
• Contribute to curriculum and teaching support for relevant graduate courses at SSHSPH.

Additional Opportunities

The group actively supports career development through conference travel, training workshops, and mentorship from senior researchers and international collaborators.

Qualifications

• PhD in statistics, biostatistics, computational biology, epidemiology, computer science, or a closely related discipline.
• Minimum of two years of postdoctoral experience.
• Demonstrable expertise in Bayesian inference and probabilistic modelling; experience with Stan, PyMC, NumPyro, Turing, or equivalent PPLs.
• Strong track record of publications in peer-reviewed journals commensurate with career stage.
• Proficiency in Python and/or R; familiarity with high-performance and cloud computing environments.
• Experience in at least two of: phylodynamics / genomic epidemiology, deep learning for sequence or tabular data, reinforcement learning, spatial modelling, or real-time nowcasting.
• Demonstrated ability to supervise junior researchers and contribute substantively to grant applications.
• Excellent written and oral communica