Job Title: Research Assistant (EID/PBC)

University-Level Unit: Duke-NUS Medical School

Faculty/Department-Level Unit: Office of Research

Employee Category: Research Staff

Location_ONB: Outram Campus

Posting Start Date: 21/10/2026

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 selected candidate will perform a variety of research activities which include planning, organising and conducting research studies within the overall scope of a research project under the supervision of the Principal Investigator or his/her designate, including but not limited to the following: -

Develop and improve framework of virome analyses, transcriptomic data analyses, metagenomic analyses or associated tools for studying infectious diseases.
Apply and integrate the tools to jointly analyse multi-omics data, including host immune responses and virome composition, aiming to characterise infection types and predict patient outcomes in acute respiratory infection.
Develop computational scripts and/or pipelines for data analyses.
Interface and network with other scientists to achieve the goals of the project.
Prepare data for manuscripts, progress reports and grant applications.
Demonstrate and present research output.
Perform other related duties incidental to the project theme.

Job Requirements

Bachelor's Degree or Master's Degree in Bioinformatics, Biostatistics, or other related disciplines.
Prior experience in designing or developing pipelines for the analysis of large-scale metagenomic or meta-transcriptomic datasets is preferred.
Have basic understanding of sequencing data formats such as FASTQ, and experience with quality control and read trimming using tools such as FastQC and fastp.
Prior experience in analysing bulk RNA-seq data, including read alignment and quantification using tools such as STAR.
Prior experience analysing single-cell RNA-seq data using tools such as Cell Ranger and Seurat.
Familiarity with public sequence databases and repositories such as SRA and NCBI, including retrieving and processing publicly available datasets.
Prior exposure to machine learning or deep learning approaches (for example scikit-learn, PyTorch or TensorFlow) and their application to biological or clinical data would be advantageous.
Familiarity with pathogenic viruses is desirable.
Possess strong knowledge in Linux/Unix operating systems and high-performance computing clusters.
Have in-depth knowledge of Bash, R and Python scripting.
Self-motived, able to work effectively and efficiently, willing to work hard to meet tight deadlines and work collaboratively in a diverse workforce.

We regret that only shortlisted candidates will be notified.

Req ID: 34696