Job Description
About the Role
The Centre for Epidemic Response & Modelling (CERM) at NUS Saw Swee Hock School of Public
Health seeks a Research Fellow with deep expertise in Bayesian statistical modelling and genomic
epidemiology to contribute to methodological and applied research across several funded
programmes. Core themes include the integration of genomic, serological, and epidemiological
data into unified Bayesian frameworks; real-time inference of transmission dynamics and variant
fitness; and the development of semi-mechanistic models for arbovirus lineage surveillance across
South and South-East Asia.
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
• Develop and extend Bayesian semi-mechanistic renewal equation models for estimating
genotype-specific reproduction numbers and immune escape.
• Build scalable inference pipelines integrating genomic (phylogenetic/lineage) data with
epidemiological surveillance streams.
• Apply and extend deep generative modelling approaches (e.g. normalising flows,
variational inference) to handle large-scale pathogen genomic datasets.
• Contribute to real-time dashboards and toolkits for arbovirus genomic surveillance (DENV,
ZIKV) across the region.
• Publish findings in high-quality peer-reviewed journals; present at international
conferences.
• Co-supervise Research Associates and Research Assistants; contribute to grant writing
and reporting.
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 epidemiology, bioinformatics, or related field.
• Strong hands-on experience with probabilistic programming (Stan, PyMC, NumPyro,
Turing, or equivalent).
• Proficiency in Python and/or R; experience with JAX or similar autodiff frameworks is
advantageous.
• Familiarity with at least one of i) phylodynamic methods (BEAST, IQTREE, or equivalent),
ii) lineage classification frameworks, and iii) renewal equation or compartmental
transmission models.
• Track record of peer-reviewed publications.
• Strong quantitative and coding skills; ability to work in collaborative, multi-institutional research settings.
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).
