The incumbent will work as part of an established and nationally funded multi-disiplinary research group that focuses on hepatocellular carcinoma and other cancers. You will be involved in a large-scale international translational and clinical study on HCC, which utilizes various types of patient biosamples and employs multi-omics approaches including bulk tumor next-generation sequencing (NGS), cfDNA NGS, single cell RNAseq and proteomics, CyTOF, 3D spatial transcriptomics and targeted/untargeted metabolomics in the context of a clinical cohort. Experience with conducting spatial transcriptomics analyses is a must. The incumbent will also be expected to work within a sandbox environment using AWS.
The candidate will assist the Principal Investigator in conceptualizing and conducting research projects. You will work collaboratively with scientific and clinical staff on data analysis and interpretation of in-house data by using various analytics tools and setting up analysis pipelines. The candidate will also be responsible for the coordination and management of the genomic projects of the multidisciplinary study, and to lead grant applications and manuscript preparation.
Your responsibilities will include:
- Multi-omics integrative bioinformatics analyses to establish the pipelines and run the analyses independently.
- Work within sandbox environment, secure, cloud environment to facilitate cross-institutional collaboration.
- Conceptualise research projects with PI and able to lead discussion across stakeholders.
- Lead in grant application and manuscript drafting.
Requirements:
- PhD degree in Computational Biology or a quantitative background (e.g. Statistics, Physics, Engineering), or Biomedical-related disciplines with a strong evidence of genomics computational experience.
- Candidates with MSc in Computational Biology or related discipline but with significant research experience may be considered.
- Experience with large-scale bioinformatics analysis (including, but not restricted to NGS, single cell sequencing, cfDNA analysis, survival analysis and disease modelling).
- Experience with conducting spatial transcriptomics analyses as follows but not limited to:
- Image analysis using tools such as Fiji, Imaris, and QuPath.
- Analyzing spatial transcriptomic data with tools including Seurat, Scanpy, Squidpy, LisaCLust, stLearn, SCDNEY, GPTcelltype, SingleR, scPred, and InSituType.
- Performing gene expression and pathway analysis on sequencing data, including bulk RNA-seq and single-cell RNA-seq, as well as sourcing and integrating publicly available sequencing datasets.
- Developing custom computational pipelines for spatial data analysis.
- Proficient in R (tidyverse, ggplot, bioconductor) or Python (pandas, NumPy, Scikit-learn) and good knowledge of Unix shell.
- Knowledge with high performance computing (HPC) and cloud computing (AWS) will be an advantage.
- Experience and knowledge in database management and data architecture will be an advantage.
- Possess excellent analytical, technical and problem-solving skills
- Well organized and detail oriented.
- Pleasant personality and be able to work with diverse groups of people.
- Excellent written and verbal communication skills.
- Good team worker and fast learner.
