Research Fellow (CBDS/BC)

Job Description Duke-NUS is seeking a highly motivated Research Fellow to join the team of A/Prof Bibhas Chakraborty at Duke-NUS, working in collaboration with A*STAR on the Singapore National Life Course Research Database (SG-NEST) programme. SG-NEST is led by Prof Neena Modi from the Institute for Human Development and Potential (IHDP), A*STAR. SG-NEST is a national programme aimed at creating research-ready datasets curated from extracts of real-world electronic health data, with linkage, where required, to other sources of administrative and research data. The utility of SG-NEST will be demonstrated through a series of use-cases. The Research Fellow will be based at Duke-NUS and work under the direction of A/Prof Bibhas Chakraborty, in close collaboration with other SG-NEST study team members. The successful candidate will analyse large and complex datasets, including electronic health records; design scalable algorithmic frameworks; simulate data from in-silico versions of novel adaptive clinical trials, such as Sequential Multiple Assignment Randomized Trials (SMART); and contribute to high-quality research publications. This position offers the opportunity to contribute to a major national research initiative through a Duke-NUS–A*STAR collaboration, working with multidisciplinary researchers and clinicians on innovative statistical and computational approaches to health and life-course research. The position can be for up to three years, potentially extendable to five. The initial contract will be for one year, with renewal subject to satisfactory performance. Job Requirements • PhD in Statistics, Biostatistics, Computer Science (Machine Learning, Artificial Intelligence), Applied Mathematics or related quantitative field with some relevant work experience. • Demonstrated knowledge and expertise in statistics, clinical trial design methodology, and machine learning /artificial intelligence models. • Prior knowledge or experience in one or more of the following areas is highly desirable: Bayesian statistics, time series analysis, causal inference, neural networks, diffusion models, digital twins and reinforcement learning. • Demonstrated knowledge and expertise in statistical computing, coding (R/Python), handling large electronic health records, and data simulation. • Prior publication records in top methodological / computational journals. • Strong communication and collaborative skills with the ability to work harmoniously with other team members as well as with collaborators from other disciplines. • Experience of clinical research and working with clinical teams is highly desirable We regret that only shortlisted candidates will be notified.