Assistant Professor

6 days ago

Singapore National University of Singapore Full time

Overview
Assistant Professor / Associate Professor (Tenure Track) (CQM/BC) at Duke-NUS | National University of Singapore. The position invites applications for a Tenure Track faculty role at the rank of Assistant or Associate Professor, focusing on Artificial Intelligence (AI) in medical or biomedical sciences with demonstrated excellence in methodological research and clinical application of AI/ML. The successful candidate will be based at the Centre for Biomedical Data Science (CBDS), effective January 1, 2026, following the strategic merger of the Centre for Quantitative Medicine (CQM) and the Centre for Computational Biology (CCB). The incoming faculty member will participate in the Duke-NUS AI + Medical Sciences Initiative (DAISI).
Key Responsibilities
Lead an innovative research programme in AI/ML, with applications in medicine, clinical research, omics, imaging, or population health.
Translate and implement advanced analytic approaches (e.g., multimodal AI) into clinical/biomedical settings with SingHealth/NUS collaborators.
Engage in collaborative research with clinicians, biomedical researchers, and statisticians across SingHealth, NUS and Duke University.
Mentor and supervise PhD students, promoting local capacity and excellence in methodology and translational AI.
Actively contribute to grant acquisition, research leadership, and dissemination of results through high-impact publications.
Support teaching in Biostatistics, Health Data Science, and related programs.
Qualifications
PhD in Statistics, Biostatistics, Computer Science, or related quantitative discipline, with strong evidence of applying AI in biomedical/clinical contexts.
Proven publication record in reputable journals and recognition for methodological and/or applied contributions in healthcare AI.
Solid record of research grant activity, both as principal investigator and co-investigator.
Excellent problem-solving skills and ability to communicate effectively across disciplines.
Demonstrated experience with multidisciplinary projects, particularly at the intersection of method development and clinical/population health application.
Prior teaching and supervision experience at the graduate level is preferred.
What We Offer
Start-up funding, commensurate with rank and the proposed research programme.
Access to secure clinical and multi-omics data environments.
Modern GPU and high-performance computing resources, plus dedicated research-engineering support.
Close integration with clinicians and clinical trial/implementation units.
Support for commercialization, intellectual property and industry partnerships.
Competitive salary and benefits, with relocation support where applicable.
How To Apply
Submit your CV, a brief 2-page research statement, and up to 3 key publications. Application review continues until filled.
Contact

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