Research Fellow in Bayesian Inference and Genomic Epidemiology

About the Position

The Centre for Epidemic Response & Modelling (CERM) at the Saw Swee Hock School of Public Health, National University of Singapore, invites applications for a Research Fellow with strong expertise in Bayesian statistical modeling and genomic epidemiology. The role involves methodological and applied research focusing on integrating genomic, serological, and epidemiological data using Bayesian frameworks, real-time inference of transmission dynamics including variant fitness, and development of semi-mechanistic models for arbovirus lineage monitoring across South and South-East Asia.

The Research Fellow will collaborate with Asst. Prof. Swapnil Mishra, Deputy Director of CERM and AI for Public Health Programme, along with a broad network of partners from NUS, Imperial College London, Ashoka University, Singapore's Communicable Diseases Agency and National Environment Agency, the Machine Learning & Global Health Network, and other international collaborators.

Key Responsibilities

• Design and enhance Bayesian semi-mechanistic renewal equation models to estimate genotype-specific reproduction numbers and immune evasion.
• Develop scalable inference pipelines combining genomic (including phylogenetic and lineage) data with epidemiological surveillance datasets.
• Utilize and advance deep generative modeling methods such as normalizing flows and variational inference for large pathogen genomic datasets.
• Support the creation and maintenance of real-time dashboards and tools dedicated to arboviral genomic surveillance (including dengue virus and Zika virus) across the region.
• Produce high-caliber scientific publications and present research outcomes at international scientific forums.
• Mentor junior research staff including Research Associates and Research Assistants, contribute to grant proposals, and manage reporting obligations.

Additional Opportunities

The group encourages professional growth by facilitating conference attendance, providing training workshops, and offering mentorship from experienced senior researchers and global collaborators.

Qualifications

• Doctorate degree in statistics, biostatistics, computational epidemiology, bioinformatics, or related disciplines.
• Extensive practical experience with probabilistic programming platforms such as Stan, PyMC, NumPyro, Turing, or similar.
• Proficiency in Python and/or R programming languages; familiarity with JAX or comparable automatic differentiation frameworks is preferred.
• Experience in at least one of the following areas: phylodynamic tools (BEAST, IQTREE or equivalents), lineage classification systems, or renewal equation and compartmental infectious disease transmission models.
• Documented history of publication in peer-reviewed scientific journals.
• Strong quantitative analysis and coding skills, with the ability to thrive in collaborative multi-institutional research teams.

Application Materials

• Cover letter detailing interest in the role, related experience, and future research vision.
• Comprehensive curriculum vitae including a complete list of publications.
• Research statement (up to two pages) summarizing past work and future research plans.
• Contact details for two professional referees; reference letters may be requested.