Interested applicants are invited to apply directly at the NUS Career Portal. Please note your application will only be processed if you apply via NUS Career Portal.
NUS Career Portal link: https://careers.nus.edu.sg/job/Senior-Research-Fellow-%28Infectious-Disease-Modelling-&-AI-for-Public-Health%29/32500-en_GB/
We regret that only shortlisted candidates will be notified.
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 communication skills; ability to engage clinical, public health, and policy audiences.
Interested applicants should submit the following documents:
• A cover letter explaining your interest in the position, relevant experience, and research vision.
• A comprehensive curriculum vitae, including a full list of publications.
• A research statement (maximum two pages) outlining past contributions and future directions.
• Contact information for two professional references (letters may be requested).
Review of applications begins immediately and continues until the position is filled. The anticipated start date is flexible by negotiation.
