Research project: Connecting within-host viral dynamics with the epidemiology of COVID-19: a multiscale computational infrastructure

In this project, we aim to develop a multiscale modeling framework and computational infrastructure to connect within-host viral dynamics/antibody dynamics and between-host transmission dynamics. The multiscale model will help address key epidemiological and public health questions which could not be answered by classic (single scale) approaches. Specifically, we plan to develop and calibrate the multiscale model for SARS-CoV-2 and assess effect of intervention (e.g., antiviral treatment) on epidemiological dynamics.

This project is led by Keisuke Ejima, an Assistant Professor at Lee Kong Chian School of Medicine, Nanyang Technological University.

The Research Fellow primarily leads the project under supervision of PI in the areas of collecting clean data, reviewing literatures, lead model development, lead data analyses, leading the manuscript writing and other areas of the project support.

Candidates should have a PhD degree in a quantitative field, such as data science, computational biology, mathematics, computer science, (bio)statistics, or related field. Research experience and/or educational background on public health and medicine is a plus but not essential. Computational experience on R and MONOLIX is preferred. Good communication skill and respectful attitude for teamwork. The successful applicant will work as part of a growing and energetic team, focusing on quantitatively understanding biological mechanisms of infection and its impact on both individual and population health.