Research Assistant (EID/PBC)

Duke-NUS Medical School; Singapore; On-site; SGD 50,000 - 80,000; Full time

Benefits offered by this job

Health insurance

Job summary

The Duke-NUS Medical School invites applications for a research role within the Signature Research Programme in Emerging Infectious Diseases. The successful candidate will plan and conduct studies under supervision, focusing on virome analyses, metagenomics, and multi-omics integration to understand host–pathogen interactions.

Applicants should hold a Bachelor’s or Master’s in bioinformatics or related fields, with experience in RNA-seq analysis, HPC, and scripting in Bash, R, and Python.

Qualifications

• Bachelor’s or Master’s degree in Bioinformatics, Biostatistics, or related fields.
• Experience designing pipelines for large-scale metagenomic or meta-transcriptomic data preferred.
• Knowledge of FASTQ formats and QC tools such as FastQC and fastp.
• Experience with bulk RNA-seq data and tools like STAR.
• Experience with single-cell RNA-seq data using Cell Ranger and Seurat.
• Familiarity with public sequence databases (SRA, NCBI) and data retrieval.
• Exposure to machine learning or deep learning approaches (scikit-learn, PyTorch, TensorFlow).
• Proficiency in Linux, HPC, Bash, R and Python scripting.
• Self-motivated, able to work under tight deadlines and collaborate in a diverse team.

Responsibilities

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

Skills

Bioinformatics

Biostatistics

Linux/Unix

Bash

R programming

Python programming

Machine learning

Deep learning

High-performance computing

Team collaboration

Education

Bachelor's or Master’s in Bioinformatics/Biostatistics or related

Advanced degree preferred

Tools

FastQC

fastp

STAR

Cell Ranger

Seurat

SRA

NCBI

PyTorch

TensorFlow

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 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.