Research Assistant (EID/PBC)

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 Requirement

• 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.