- Title: Computational Biologist
- Code: RCI-19631
- Location: Mississauga, ON L5N 5M8
- Posted Date: 09/23/2022
- Duration: 12 Months
Talk to our Recruiter
- Name:Jeremy Day
- Email: firstname.lastname@example.org
- Phone: 908-704-8843 ✖
- The Department of Cell and Tissue Genomics (CTG) is seeking a highly talented, creative, and motivated Computational Biologist to develop and apply novel machine learning methods for the analysis of cutting-edge high-dimensional profiling and screening data.
- The candidate will closely collaborate with members of the department to extract biological insights from diverse high-dimensional data sets through advanced machine learning and AI methods.
- The successful candidate will work in a highly collaborative team with colleagues in the bioinformatics and AI & Machine Learning departments and across the therapeutic areas.
- Minimum of a Master’s Degree or PhD in bioinformatics, computational biology, quantitative biology, computer science, or related fields.
- Strong computational and data analysis skills, including proficiency with Python and strong experience with the analysis of omics data, image data, and/or sequence data (e.g., Natural Language Processing)
- Demonstrated experience with modern Python frameworks for deep learning (e.g. PyTorch, TensorFlow-Keras, or JAX)
- Experience with single cell RNA-Seq and/or single cell ATAC-Seq analysis
- Comfortable with modern Linux and HPC environments, tools for data analysis, visualization, statistics, analysis pipelines (Snakemake or Nextflow), and version control tools such as git
- Good understanding of biological concepts and an interest to learn more
- Excellent oral and written presentation skills, creative problem-solving abilities, attention to detail, good communication and collaboration skills
Preferred Skills and Experience:
- Experience in analyzing CRISPR/perturbation screens
- Experience with spatial transcriptomics data analysis
- Experience with applying various machine learning models to high throughput datasets
- Perform comprehensive and statistically sound analyses of various single cell datasets to generate biological insights
- Develop and apply machine learning methods and pipelines for the analysis of omics and/or image data, including novel and state-of-the-art acquisition technologies such as spatial transcriptomics and high-content perturbation screens