Advancing Medicine through Scientific Discovery
PhD in Quantitative Biology and Medicine (QBM)
The PhD programme in Quantitative Biology and Medicine (QBM) offers a research-focused curriculum with foundation courses, discipline-specific courses and research seminars. Students will graduate with expertise that is in high demand and essential for biomedical research and health data science.
Students in the QBM programme will select a concentration in either Computational Biology or Biostatistics and Health Data Science:
- Computational Biology integrates data analytics, statistics, machine learning, modelling, software engineering and computer science to answer questions in basic and translational biomedical research. The Computational Biology concentration prepares students for careers in computational and data-driven biomedical research. It comprises all areas of current bioinformatics practice and research, including genome informatics, bioinformatics for next-generation sequencing, modelling of biological processes, and image analysis for biology, including neuroimaging.
- Biostatistics and Health Data Science aims to advance biomedical and health sciences through the development and application of innovative quantitative methods, including biostatistics, statistical learning, and artificial intelligence. The concentration focuses on study designs and analytic methods for answering questions in genomics, clinical, epidemiological and health services research. Students in the programme will master specialised areas in data science and their applications in biomedical and health studies.
Students take between four and five years to complete the programme and are awarded a PhD degree by NUS.
Applications to the PhD programmes open in June of the preceding year of intake, and 15th January is the final deadline.
|Month||PhD Application Timeline|
|Jun||Applications for next year's intake opens|
|Jan||Application deadline for PhD programmes: 15 January*|
|Mar||Admissions offers are made|
|Aug||Class is confirmed
*Interested applicants are strongly encouraged to submit their application as early by 1st January should they wish for their application to be reviewed and notified earlier for shortlisting.