Deskripsi Pekerjaan
Are you ready to redefine the landscape of oncology through computational precision? The Agency for Science, Technology and Research (A*STAR) is seeking a highly motivated Scientist to join our Genome Institute of Singapore (GIS) team. In this role, you will be at the forefront of cancer research, utilizing advanced AI and machine learning techniques to decode complex genomic structural variations.
You will work within a world-class facility equipped with cutting-edge sequencing technologies and high-performance computing clusters. This role is dedicated to the reconstruction of cancer genomes, bridging the gap between raw sequencing data and actionable clinical insights. If you are passionate about genomic instability, structural evolution, and developing novel algorithms to solve the most challenging problems in cancer biology, we invite you to apply.
Tanggung Jawab
- Lead the development and implementation of advanced algorithms for de novo genome assembly and structural variation analysis.
- Integrate multi-omics datasets to improve the accuracy of cancer genome reconstruction models.
- Collaborate with interdisciplinary teams of biologists, clinicians, and data scientists to translate research findings into diagnostic tools.
- Develop robust pipelines for high-throughput genomic data processing and quality control.
- Present research findings at international conferences and publish in high-impact peer-reviewed journals.
- Mentor junior researchers and students in computational genomics and bioinformatics workflows.
- Maintain and optimize codebases for scalability within cloud-based infrastructure.
Kualifikasi
- PhD in Bioinformatics, Computational Biology, Computer Science, or a related quantitative field.
- Proven track record in developing algorithms for genomic data analysis (e.g., assembly, alignment, variant calling).
- Strong proficiency in programming languages such as Python, R, C++, or Rust.
- Expertise in utilizing high-performance computing (HPC) environments and cloud platforms (AWS/GCP).
- Deep understanding of cancer genomics, specifically structural variants and copy number alterations.
- Experience with machine learning frameworks such as PyTorch or TensorFlow for biological data.
- Excellent communication and collaborative skills with the ability to work in a dynamic team environment.