Research Fellow (CBDS/BC)

NATIONAL UNIVERSITY OF SINGAPORE; Singapore; On-site; SGD 90,000 - 140,000; Full time

Job summary

Duke-NUS is seeking a Research Fellow to join A/Prof Bibhas Chakraborty's team, working with A*STAR on the SG-NEST programme. SG-NEST is led by Prof Neena Modi from IHDP, A*STAR.

The Research Fellow 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 SMART; and contribute to high-quality research publications.

Qualifications

• PhD in statistics/biostatistics/computer science, or related quantitative field with some work experience.
• Knowledge in statistics, clinical trial design and ML/AI models.
• Desirable: Bayesian stats, time series, causal inference, neural networks, diffusion models, digital twins, RL.
• Experience in statistical computing, coding (R/Python), large health data, data simulation.
• Prior publications in methodological/computational journals.
• Strong communication and collaboration skills with cross-disciplinary teams.

Responsibilities

• Analyse large and complex datasets including electronic health records.
• Design scalable algorithmic frameworks for health data science.
• Simulate data from in-silico models and adaptive trial designs.
• Contribute to high-quality research publications.

Skills

Statistics

Clinical trial design

Machine learning

AI models

Bayesian statistics

Time series analysis

Causal inference

Neural networks

Reinforcement learning

Data simulation

R/Python coding

Handling large EHRs

Scientific publications

Communication and collaboration

Education

PhD in Statistics

PhD in Biostatistics

PhD in Computer Science (ML/AI)

PhD in Applied Mathematics

Tools

R

Python

Job description

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.