Computational Biologist
Found in: beBee jobs CA - 1 week ago
The Compensation Range is the span between the minimum and maximum base salary for a position. The midpoint of the range is approximately halfway between the minimum and the maximum and represents an employee that possesses full job knowledge, qualifications and experience for the position. In the normal course, employees will be hired, transferred or promoted between the minimum and midpoint of the salary range for a job.
Posting End DateMarch 14, 2024Note: Applications will be accepted until 11:59 PM on the day prior to the Posting End Date above.
Job End Date
Mar 31, 2025At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and creates the necessary conditions for a rewarding career.
Job Summary
Join our dynamic academic team (Drs. Cembrowski, de Boer, Hirst, Hoodless, Shakiba, Underhill and Yachie) as a Computational Biologist specializing in the development and implementation of various computational tools for the analysis of omics datasets. This role offers an exciting opportunity to contribute to cutting-edge research at the intersection of computational biology, omics, and machine learning. The group utilizes a variety of model systems combined with single cell omics to interrogate fundamental mechanisms underpinning health (i.e., tissue renewal, homeostasis and regeneration) and disease (i.e., neurodegeneration, fibrosis, cancer).
Organizational Status
The incumbent reports to the Principal Investigator, Dr. T. Michael Underhill and works closely with the other researchers in our team; Drs. Cembrowski, de Boer, Hirst, Hoodless, Shakiba, and Yachie.
Work Performed
- Operating and improving a state-of-the-art next generation sequencing data analysis pipeline.
- Performing sequence alignments, file merges and quality filtering using standardized tools.
- Implement robust computational pipelines for processing, analyzing, and interpreting single cell omics datasets along with newly emerging spatial transcriptomic data.
- Collaborate with researchers to understand experimental objectives and tailor computational solutions to address specific biological questions.
- Implement machine learning algorithms to extract meaningful insights from complex datasets and contribute to predictive modeling efforts.
- Optimize existing computational workflows and integrate novel methodologies to enhance data analysis efficiency and accuracy.
- Document methodologies, algorithms, and results for dissemination within the academic community through publications and presentations.
- Contributing to scientific grant proposals, presentations and publications.
- Other related duties as required.
Application Process: Interested candidates should submit a cover letter, CV, and contact information for references to Please include "Computational Biologist Application" in the subject line.
Consequence of Error/Judgement
The impact of decisions and consequences of errors are significant. Errors could lead to loss of partner relationships, funding or affect the effectiveness of the research projects and reputation of the research team.
Supervision Received
The position works independently under broad directives from the research team, and work will be periodically reviewed by the team.
Supervision Given
Provide direction and technical knowledge to a research team or provide functional direction to researchers. Supervise and train students and other staff as required.
Minimum Qualifications
Post-graduate degree in Statistics. Minimum of two years of related experience in research analysis, or the equivalent combination of education and experience.
- Willingness to respect diverse perspectives, including perspectives in conflict with one's own
Demonstrates a commitment to enhancing one's own awareness, knowledge, and skills related to equity, diversity, and inclusion
Preferred Qualifications
- Experience in working in a Unix environment and proficiency in programming languages commonly used in bioinformatics (e.g., Python, R) and experience with relevant libraries and tools (e.g., Bioconductor, scikit-learn).
- Demonstrated expertise in analyzing various omics datasets (bulk RNA-seq, scRNA-seq, scATAC-seq, etc.).
- Strong understanding of statistical methods, data mining, and machine learning techniques, with a focus on their application in biological contexts would be an asset although not a requirement.
- Excellent communication skills and the ability to collaborate effectively with interdisciplinary teams.
- Experience in developing and deploying computational pipelines in high-performance computing environments.
- Track record of publications showcasing innovative contributions to the field of computational biology would be an asset, but not required.
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