Scientist - Cancer Genome Reconstruction (GIS)

A*STAR Research Entities (A*STAR); Singapore; On-site; SGD 70,000 - 110,000; Full time

Benefits offered by this job

Fully funded position

Professional development opportunities

Access to HPC clusters

Dynamic, interdisciplinary environment

Two-year appointment

Job summary

Genome Institute of Singapore (GIS) invites applications for a highly motivated postdoctoral researcher in the Laboratory of AI in Genomics. The role focuses on assembling cancer genomes using traditional algorithms and AI, with emphasis on graph-based approaches and cross-genome comparisons for improved cancer genomics analyses.

Join a collaborative team led by Prof. Mile Sikic, working at the heart of Singapore's biomedical hub.

Qualifications

• PhD in computer science, applied mathematics, or related field.
• Proven experience in algorithms on genome sequences and graphs.
• Publication record in top-tier journals or bioinformatics conferences.
• Strong experience in C/C++, Rust and Python programming and solid software engineering skills.
• Structured, independent, proactive and collaborative working style.

Responsibilities

• Develop overlap between reads from cancer and normal genomes.
• Simplify cancer genome graphs while preserving normal genome haplotypes and subclonal haplotypes.

Skills

C/C++/Rust

Python

Algorithms

Research

Publications

Education

PhD in CS or related field

Job description

The Genome Institute of Singapore (GIS) is the national flagship for genomic sciences, driving cutting-edge research at the intersection of biology, engineering, and medicine. This position is offered in the Laboratory of AI in Genomics, led by Prof. Mile Sikic, which uses advanced bioinformatics and deep learning approaches to develop next‑generation models for genomic data analysis. We are group a of computer scientist with a mission to improve healthcare using advance deep learning models. Located in the heart of Singapore's thriving biomedical hub, GIS offers a dynamic and collaborative environment, with close ties to world‑class universities (NUS and NTU), pharmaceutical companies, and biotech start‑ups. Joining our team means working on transformative projects with real‑world impact, while benefiting from Singapore's vibrant research ecosystem and strong support for innovation.

Project background

De novo genome assembly has been one of the most challenging problems in genomics. However, with development of new long read sequencing technologies, new assemblers and manually curated high quality benchmarks including CHM13, HG002 and I002C assembling most of human chromosomes T2T has become a routine task.

This project will focus on a more challenging task, assembling of human cancer genomes which are especially difficult due to cancer genome heterogeneity. Using the fact that we can reconstruct normal tissue genomes routinely we aim to reconstruct cancer genomes using a combination of traditional algorithms and AI.

We are looking for a highly motivated postdoctoral researcher to:

• Develop overlap between reads sequences from both cancer and normal genomes
• Simplify cancer genome graphs keeping a both normal genome haplotypes and subclonal haplotypes in a graph

Profile

We welcome applications from candidates with:

• A PhD in computer science, applied mathematics, a related field
• Proven experience in algorithms on genome sequences and graphs
• Publication record at top-tier journals or bioinformatics conferences (i.e. Recomb, ISMB, EECB and Genome Informatics)
• Strong experience in C/C++/Rust and Python programming and solid software engineering skills
• A structured, independent, proactive and collaborative working style

We offer

• A fully funded position with an internationally competitive salary
• Professional development opportunities, including support for grant applications and participation in conferences and workshops
• Access to state-of-the‑art research infrastructure, including NSCC's high-performance computing clusters
• A dynamic, interdisciplinary, and collaborative research environment
• The position is initially offered for two years