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"Research Fellow (CBDS/SXY)"

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Research Fellow (CBDS/SXY)

Research Fellow

2026-05-01

Location

Outram Campus

Duke-NUS Medical School

Type

Academic / Faculty

Required Qualifications

PhD in Computer Science, Biostatistics, Bioinformatics, or AI-related field
Strong AI, ML, statistical modeling expertise
Proficiency in R, Python, or Julia
Excellent communication & collaboration skills
Publication record (advantage)
Omics data experience (advantage)

Research Areas

Multi-omics data analysis (DNA, RNA, protein, single-cell)
AI & statistical methods
Cancer, cardiometabolic diseases, neurodegeneration
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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. Song Xiaoyu’s laboratory is seeking a highly motivated Research Fellow with expertise in computer science, biostatistics, statistics, bioinformatics, computational biology, or other AI-related disciplines. The successful candidate will join a dynamic, interdisciplinary team focused on developing and applying novel AI and statistical methods for multi-omics data analysis. This position offers a unique opportunity to work at the forefront of biomedical data science and contribute to discoveries that advance the understanding of cancer, cardiometabolic diseases, neurodegeneration, and other complex conditions.

More information about Dr Song Xiaoyu can be found at https://sites.google.com/view/xiaoyu-song/home.

Key Responsibilities:

  • Design and develop AI and statistical methods for complex omics data, including multi-modality (e.g. DNA, RNA, protein, PTM, metabolites) in different resolutions (e.g. 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 clinicians, biologists, computer scientists, biostatisticians, students and other research staff.
  • Prepare oral presentations, posters, 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. Develop skills and experience to write mentored and independent research grants.
  • Perform other related duties incidental to the work described herein.

Job Requirements

  • PhD in Computer Science, Biostatistics, Statistics, Bioinformatics, Computational Biology, or other AI-related disciplines.
  • 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.

More Information

Location: Outram Campus

Organization: Duke-NUS Medical School

Department: Office of Research

Employee Referral Eligible: No

Job requisition ID: 31881

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Frequently Asked Questions

🎓What qualifications are required for this Research Fellow position at Duke-NUS?

Candidates must hold a PhD in Computer Science, Biostatistics, Statistics, Bioinformatics, Computational Biology, or other AI-related disciplines. A strong foundation in AI, machine learning, statistical modeling, and high-dimensional data analysis is essential. Proficiency in programming languages like R, Python, or Julia is required, along with excellent communication skills. Strong publication records and experience with biological data (e.g., genomics, transcriptomics, single-cell) are advantageous. Explore more on postdoc opportunities or research jobs for similar roles.

🔬What are the key responsibilities in Dr. Song Xiaoyu’s laboratory?

Responsibilities include designing AI and statistical methods for multi-omics data (e.g., DNA, RNA, protein, single-cell, spatial), analyzing large-scale datasets, generating biological insights, collaborating with interdisciplinary teams (clinicians, biologists), preparing presentations/reports, leading high-impact publications, and assisting with grant applications. This role focuses on advancing research in cancer, cardiometabolic diseases, and neurodegeneration. Check postdoctoral success tips for thriving in such positions.

💻What programming and technical skills are needed for multi-omics analysis?

Proficiency in R, Python, or Julia is required for developing AI models and handling high-dimensional omics data. Expertise in machine learning, biostatistics, and bioinformatics tools for multi-modality data (subject/tissue/single-cell levels) is crucial. Experience with genomics, transcriptomics, or single-cell data is an advantage. Learn more via research assistant jobs or research skills advice.

📅How to apply for this Research Fellow role and what is the deadline?

Applications are open until the expiration date of 2026-05-01. Only shortlisted candidates will be notified. Submit via Duke-NUS channels with your CV highlighting PhD, publications, and relevant experience. Prepare for interdisciplinary collaboration in biomedical data science. Visit Dr. Song’s lab at xiaoyu-song/home. Tailor your application using our free resume template.

🧬What research areas does this position cover at Duke-NUS CBDS?

The role focuses on multi-omics data analysis using novel AI and statistical methods for complex diseases like cancer, cardiometabolic diseases, and neurodegeneration. Work with multi-modality data (DNA, RNA, protein, metabolites) across resolutions, including spatial features. Contribute to high-impact publications and grants in the Centre for Biomedical Data Science (CBDS). Discover similar roles in clinical research jobs or faculty positions.

📊Is prior experience with biological data necessary?

While not strictly required, experience with biological data such as genomics, transcriptomics, or single-cell omics data is an advantage. A strong foundation in AI, machine learning, and programming compensates, with opportunities to build expertise in Dr. Song’s lab. Strong publication records enhance candidacy. Read academic CV tips to strengthen your profile.

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