Computational Biologist (BD-MED)

We are recruiting a Research Fellow (Computational Biologist) at the Cancer Discovery Hub, National Cancer Centre Singapore.You will be involved in various “omics” projects based on Next Generation Sequencing, single-cell technologies, as well as spatial profiling. You will be required to perform data management, extraction, mining and analysis of NGS data, and preparation of research publications/grant applications, and expected to lead highly impactful projects in translational oncology and cancer science.

Your responsibilities will include:
- Perform data management, extraction, mining and analysis of Next Generation Sequencing data.
- Preparation of research publications/grant applications.
- Assist with general upkeep of laboratory needs, provide technical support and establish work procedures to ongoing research projects. Support all ancillary activities of the lab including fund raising, education and philanthropy efforts.

Requirements:
- PhD in Bioinformatics, Genomics, Maths, Physics or other related fields with proficiency with NGS data handling.
- Comfortable with Unix based OS in a High Performance Computing Cluster (HPCC).
- Comfortable with exploration/analysis/visualization of genomic data with R/Python.
- Preprocessing and analysis of multi-omics sequencing data using available bioinformatics tools/software/pipelines.
- Knowledge in analyzing single-cell and spatial omics data will be required (e.g. 10X Genomics, BGI Stereo-seq).
- Knowledge of statistical test/analysis, clinical/biological data interpretation.
- Knowledge in development of prediction models, dimensionality reduction techniques associated with analysis of high-dimensional cancer-omics data.
- A highly motivated individual displaying strong interest in cancer pan-omics and translational cancer research.
- Versatile individual with willingness to learn, good interpersonal skills and a team player.- Preprocessing and analysis of multi-omics sequencing data using available bioinformatics tools/software/pipelines.
- Knowledge in analyzing single-cell and spatial omics data will be required (e.g. 10X Genomics, BGI Stereo-seq).
- Knowledge of statistical test/analysis, clinical/biological data interpretation.
- Knowledge in development of prediction models, dimensionality reduction techniques associated with analysis of high-dimensional cancer-omics data.
- A highly motivated individual displaying strong interest in cancer pan-omics and translational cancer research.
- Highly motivated individual with willingness to learn.