Research Fellow (CBDS / SXY)

Job Description

The Duke-NUS Centre for Biomedical Data Science (CBDS) serves as a central hub for Duke-NUS faculty specialising in Biostatistics, Bioinformatics, Systems Biology, Artificial Intelligence (AI), and other quantitative and data-driven sciences. CBDS functions as a strategic platform for cutting-edge research, education, and services in data science, AI, and biomedical computation, building on existing institutional initiatives such as DAISI.

Dr. Xiaoyu Song's laboratory at Duke-NUS Medical School is seeking a motivated Research Fellow with expertise in biostatistics, bioinformatics, or computational biology. The successful candidate will join an interdisciplinary team developing novel statistical methods for multi-omics data analysis. Research areas may include method development for studying the tumour microenvironment using spatial transcriptomics, integrative proteogenomics for causal inference, and AI-driven models for cell-type-aware analysis of bulk omics data.

This position offers an opportunity to work at the forefront of biostatistics and contribute to advancing the understanding of cancer and other complex diseases.

Key Responsibilities:

• Design and develop statistical methods for high-dimensional omics data, including multi-level data (subject, tissue, single-cell) with or without spatial features.
• Analyse large-scale omics datasets, interpret results, and generate biological insights.
• Work closely with national and international collaborators, including biostatisticians, clinicians, biologists, students and other research staff.
• Prepare oral presentations and written reports to evaluate data, explain analysis methods, and interpret results.
• Lead and contribute to high-impact publications in statistical and biomedical journals.
• Assist in the preparation of grant applications and reports for ongoing funded projects.
• Perform other related duties incidental to the work described herein.

Job Requirements

• PhD in Biostatistics, Bioinformatics, Computational Biology, or related fields.
• Strong foundation in AI, statistical modeling, machine learning, or high-dimensional data analysis.
• Proficiency in programming languages such as R, Python, or Julia.
• Excellent communication and collaborative skills.
• Strong publication records in peer-reviewed journals is an advantage.
• Experience with biological data (e.g., genomics, transcriptomics, single-cell data) is an advantage.

We regret that only shortlisted candidates will be notified.