- Hospital for Sick Children
- Toronto, ON, Canada
- Job Type:
- Degree Level Required:
- Apply Now
We are looking for a highly motivated bioinformatician with practical experience in the processing and analysis of large genomic datasets including next-generation sequencing datasets to join a dynamic laboratory at the Hospital for Sick Children.
Our group focuses on the biology of brain tumours of children and adults, with a primary aim to understand how the molecular programs in normal neural stem cells cause and sustain the growth of these hard-to-treat cancers. In addition, we are interested in brain tumour heterogeneity and how the diverse cell types that comprise these tumours contribute to tumour maintenance and therapeutic resistance.
Here’s What You’ll Get To Do: • You will be expected to process and analyze large genomic datasets including next-generation sequencing datasets, including the integration of genomic (WGS,WES), transcriptomic (RNA-seq) and epigenomic (DNAm, ATAC) datasets and single cell data analysis and integration • You will work closely with both wet and dry-lab scientists to collaboratively solve complex biological problems • You will work on several large collaborative concurrent projects/priorities • You will manage the dissemination of research findings for publication in peer-review journals and conferences.
Here’s What You’ll Need: • A PhD in bioinformatics, biostatistics, computational biology or similar, with a strong publication record. Alternately, a PhD in molecular biology combined with experience of high-throughput data analysis, supported by first- author publication(s) in this area. • Additional postdoctoral experience strongly preferred. • Strong preference for candidates with additional experience in the integration of genomic (WGS,WES), transcriptomic (RNA-seq) and epigenomic (DNAm, ATAC) datasets and single cell data analysis and integration • Experience working in a Unix/Linux environment • Experience with HPC environments • Experience writing code in a high-level programming language such as R or Python for complex data analysis and visualization • Familiarity with public biomedical information resources such as genome browsers, data repositories and annotation databases • A solid understanding of the relevant concepts in cancer biology, as well as enthusiasm for further learning • Strong communication, data presentation and visualization skills