Research Fellow (OR/EID/ML3)

Duke-NUS Medical School; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Duke-NUS Medical School in Singapore invites applications for a full-time Research Fellow / Senior Research Fellow in Computational Biology within the Signature Research Programme in Emerging Infectious Diseases. The successful candidate will analyse high-dimensional omics data and contribute to interdisciplinary virology, genomics, and data science research.

You will develop AI/ML pipelines for pathogen risk assessment and collaborate with wet-lab scientists to translate computational insights

Qualifications

• PhD in related scientific area with hands-on experience in multi-omics data integration.
• Proven proficiency analyzing bulk and single-cell transcriptomic datasets.
• Experience with R and/or Python coding; HPC and GPU programming familiarity.
• Experience with spatial omics or GWAS is advantageous.
• Strong quantitative, analytical, and organizational skills; collaborative mindset.

Responsibilities

• Perform tasks and support all aspects of the research project.
• Lead AI/ML-driven pathogen risk assessment pipelines.
• Collaborate with experimental scientists to translate computational findings.
• Contribute to publications, presentations, and grant proposals.
• Mentor junior lab members and contribute to team science.

Skills

Multi-omics data integration

Single-cell data analysis

R/Python coding

HPC & GPU computing

Quantitative/analytical skills

Collaborative team science

Education

PhD in bioinformatics / computational biology / ML / CS / virology / immunology

Tools

R

Python

Job description

Job Description

The Signature Research Programme in Emerging Infectious Diseases (EID) aims to pioneer our understanding of emerging infections through world-class research that integrates immunology, infection biology, pathogen genomics, and computational approaches. This integration drives comprehensive studies of host–pathogen interactions across molecular, individual, and population scales to address emerging health challenges.
The Coronavirus Lab, led by Assistant Professor Mart Lamers within the Signature Research Programme in Emerging Infectious Diseases (EID), is seeking a full-time Research Fellow / Senior Research Fellow in Computational Biology. Our group leverages cutting-edge technologies—including organoids, CRISPR/Cas9, single-cell and spatial omics to study coronavirus–host interactions that influence pandemic potential and disease pathogenesis.

We welcome applications from candidates with a strong background in computational and quantitative biology. The successful candidate will play a key role in analysing high-dimensional datasets and fostering an interdisciplinary research environment at the interface of virology, genomics, and data science.

We are looking for a creative, innovation-driven individual who is eager to uncover hidden patterns within complex datasets—insights that can both strengthen our experimental findings and spark bold, testable hypotheses that push the boundaries of discovery in our lab. This role also offers the freedom to develop quantitative AI/ML pipelines for pathogen risk assessment, informed by insights from our wet lab scientists.

Main Responsibilities

• Perform tasks and support all aspects of the research project.
• Carry out algorithm development and data analysis, including (but not limited to) single-cell and bulk transcriptomics, spatial omics, proteomics, and genome-wide association (GWAS) data independently.
• Lead AI/ML-driven efforts to develop pathogen risk assessment pipelines.
• Collaborate with experimental scientists to integrate computational findings with biological hypotheses.
• Contribute to scientific writing and dissemination of results through publications, presentations, and grant proposals.
• Mentor junior lab members and contribute to collaborative team science efforts.
• Perform other related duties incidental to the work described therein.

Job Requirements

• PhD Degree in a related scientific area (e.g., Bioinformatics, Computational Biology, Machine Learning/AI, Computer Science, Virology, Cell biology, or Immunology) with demonstrated hands-on experience indeveloping and/or applying algorithms for multi-omics data integration.
• Proven proficiency analysing bulk and single-cell transcriptomic datasets.
• Proficiency in R and/or Python coding.
• Familiarity with high-performance computing (HPC) environments and GPU programming.
• Have experience with spatial omics or GWAS analysis is strongly advantageous.
• Experience with AI/ML approaches for biological data analysis would be advantageous.
• Have excellent quantitative, analytical, and organizational skills.
• Collaborative, motivated, and adaptable, with a commitment to open and integrative team science.

We regret that only shortlisted candidates will be notified.