Scientist, GRIDS Platform (GIS)

Background:

The Genome Institute of Singapore (GIS) is seeking a highly motivated Postdoctoral Fellow (Bioinformatics) to join Singapore's groundbreaking population-scale genomics initiative aiming to build a comprehensive data repository integrating genetic, clinical, and lifestyle data.

In its pilot phase, this effort led to the detailed characterization of genetic variation across Singapore's three main ethnic populations (Wong et al. Nature Genetics, 2023; Tan et al. Nature Communications, 2024). Moving forward, the challenge is to develop integrated analytics frameworks for the next phases of the National Precision Medicine (NPM) program, which encompassed data collected amongst 100,000 individuals and will scale up to 500,000 individuals by 2026.

By joining our multidisciplinary team, you will:

• Be part of one of the largest and most ambitious population genomics programs in Asia.
• Work with cutting-edge technologies in genome informatics, AI-driven analytics, and cloud computing.
• Collaborate with leading scientists, clinicians, and engineers in an interdisciplinary setting.
• Gain exposure to real-world applications of genomic medicine in precision health and population genetics.

Job Description and Scope of the Specific Project:

Deciphering single-nucleotide and simple genetic variation in large-scale whole-genome sequencing (WGS) datasets is well established. However, developing scalable and efficient strategies for complex multi-nucleotide variations—including structural variations, repeats, mobile elements, and low-complexity regions—remains a major challenge, especially for both short-read and long-read sequencing data.

We seek a highly motivated computational biology postdoctoral fellow to address this challenge by:

• Developing scalable computational approaches to analyse complex genetic variations at population scale.
• Co-developing novel data analytics pipelines leveraging pangenome references for future-proof genomic data analysis.
• Exploring advanced data-sharing approaches, including generative AI for synthetic genome-phenome relationship modelling.
• Implementing and optimizing high-performance computing workflows for large-scale genomic datasets.
• Collaborating with local and international partners on research and implementation of best practices (e.g., GA4GH, HPRC, gnomAD).
• Publishing research findings in high-impact journals and presenting results at conferences.

Key Qualifications:

Candidates who meet most of the following qualifications will be considered:

Required:

• PhD (or equivalent) in Bioinformatics, Computational Biology, Statistical Genetics, or related fields.
• Experience in high-throughput sequencing data analysis (especially structural variations, repeat elements, and complex genetic loci).
• Knowledge of population genetics/statistical genetics (experience with pangenome variation graphs and tools like VG and Hail is a plus).
• Proficiency in Python for automation and data analysis.
• Familiarity with workflow management systems such as Nextflow, CWL, or WDL.
• Experience with cloud computing and infrastructure (AWS preferred).
• Strong problem-solving skills, ability to learn quickly, and work in a team-oriented environment.

Preferred:

• Experience with big-data frameworks and graph-based genome representations.
• Experience with version control systems (e.g., Git/GitHub).
• Familiarity with Atlassian tools (Jira, Confluence).
• Ability to write technical documentation and contribute to internal training.

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.