Scientific Lead for Computational Biology and AI in Precision Health

University of British Columbia•Vancouver, BC

8d•CA$8,305 - CA$12,952•Onsite

About The Position

This position is designed for a senior computational biologist with extensive expertise (10+ years) in multi-omics data integration, artificial intelligence, systems biology, translational research, and collaborative data-driven research environments. The role requires advanced scientific leadership in the development and implementation of integrative analytical frameworks, including interpretable machine learning approaches, that support large-scale immunology, infectious disease, inflammation, and precision medicine initiatives within the Turvey Laboratory. The position is particularly suited to candidates with demonstrated expertise in high-dimensional biological data integration, computational analysis of complex datasets, statistical and machine learning modelling, and the architectural design of scalable, reproducible analytical workflows in collaborative research environments. The Scientific Lead for Computational Biology and AI in Precision Health is a senior scientific and technical position within the Turvey Laboratory, responsible for leading the integration, interpretation, and operationalization of complex multi-omics datasets generated through translational and clinical immunology research programs. The lab has a particular focus on identifying monogenic causes of human disease. The role provides leadership in computational biology and artificial intelligence strategy, research facilitation, and interdisciplinary collaboration across projects involving bulk and single cell genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiome, and clinical metadata. Working closely with investigators, clinicians, bioinformaticians, trainees, collaborators, and external partners, the successful candidate develops and implements scalable computational frameworks and AI-assisted workflows that support discovery science, biomarker development, precision medicine initiatives, and grant-funded research activities. The position plays a central role in enabling high-impact, data-driven research within the laboratory and across collaborative networks. The Scientific Lead for Computational Biology and AI in Precision Health exercises a high degree of initiative, independence, and professional judgment in managing scientific priorities, developing advanced analytical and machine learning workflows, establishing rigorous data governance, stewardship, and reproducibility standards, and supporting strategic research planning.

Requirements

Post-graduate degree in Statistics.
Minimum of five years of related experience in integrative 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.
PhD in bioinformatics, computational biology, systems biology, biostatistics, computer science, genomics, immunology, or a related discipline preferred.
Minimum of 10 years of post-PhD related experience in computational biology, bioinformatics, or multi-omics research environments, preferably within academic, industry, or translational research settings.
Experience integrating multiple high-dimensional biological datasets.
Experience supporting collaborative interdisciplinary research programs.
Experience with scientific project coordination and research facilitation.
Demonstrated expertise in computational biology, bioinformatics, systems biology, and multi-omics integration.
Strong background in the analysis and interpretation of high-dimensional biological datasets, including analysis for monogenic disorders.
Extensive experience developing bioinformatics pipelines, computational tools, and reproducible analytical frameworks for large-scale omics datasets.
Advanced programming and scripting expertise in R, Python, and Bash.
Extensive experience working with high-performance computing (HPC) environments (e.g., Slurm), cloud-based infrastructure, workflow management systems, and containerized solutions (e.g., Docker/Singularity).
Experience working with high-performance computing environments, cloud-based infrastructure, workflow management systems, and containerized computational workflows.
Experience integrating bulk and single-cell genomic, transcriptomic, epigenomic, proteomic, and clinical datasets in translational research settings.
Experience collaborating with interdisciplinary teams including clinicians, immunologists, computational biologists, statisticians, and research trainees.
Strong scientific publication record and demonstrated ability to contribute to high-impact collaborative research initiatives.
Experience supporting grant development, collaborative research networks, and strategic scientific planning.
Demonstrated ability to provide mentorship, technical leadership, and scientific guidance within research teams.
Ability to exercise sound judgment, initiative, diplomacy, and discretion.
Excellent organizational, interpersonal, and communication skills.
Ability to work independently and collaboratively in a fast-paced research environment.
Multi-omics data integration and systems biology
Genome analysis
Computational biology and bioinformatics analysis
Machine learning and predictive modelling in biological systems
Bulk and single-cell omics analysis
Biological pathway and network analysis
Statistical genomics and computational immunology
Reproducible research and workflow automation
Development and optimization of bioinformatics software tools
Scientific programming and data visualization
Collaborative translational research environments
Integration of clinical and biological datasets
Data stewardship, governance, and reproducibility practices
Scientific communication and interdisciplinary collaboration

Responsibilities

Architects and directs the design, development, and implementation of integrated multi-omics data analyses and artificial intelligence strategies across laboratory research programs.
Develops and deploys cutting-edge computational approaches, including interpretable machine learning models, for integrating high-dimensional genomic, transcriptomic, epigenomic, proteomic, metabolomic, microbiome, and clinical datasets.
Interprets complex, large-scale biological data to support translational research objectives in immunology, infectious diseases, inflammation, and precision medicine.
Acts as a scientific advisor to investigators and collaborators on study design, advanced computational analytical methodology, statistical considerations, and downstream data interpretation.
Identifies emerging technologies, state-of-the-art machine learning and computational biology tools, and analytical methods to maintain the laboratory’s competitive edge.
Establishes and drives strategic partnerships with internal and external institutional stakeholders to support large-scale collaborative research initiatives.
Coordinates, prioritizes, and manages data integration activities across multiple concurrent research projects.
Establishes operational timelines, priorities, deliverables, and rigorous quality standards for computational and analytical workflows.
Serves as a multi-disciplinary bridge, facilitating communication between wet-lab researchers, clinicians, computational scientists, and collaborators.
Supports grant development, contributing to research strategy, study design, preliminary analyses, budgeting, and methodological sections.
Contributes to manuscript and potentially leads preparation, scientific presentations, technical reports, and knowledge translation activities.
Participates in strategic institutional planning related to research infrastructure, high-performance computing (HPC), and data management.
Designs, implements, and maintains production-grade, scalable, and reproducible analytical pipelines, workflows, and documentation.
Oversees the stewardship, quality control, harmonization, and integration of data assets.
Establishes, mandates, and promotes best practices in institutional data governance, reproducibility, secure data management, version control (Git), and containerized reproducibility (e.g., Docker/Singularity).
Works with institutional IT and HPC teams to optimize and scale cluster infrastructure and cloud-based analytical environments.
Evaluates, benchmarks, and deploys specialized software tools, databases, and platforms to enhance laboratory research capabilities.
Provides technical mentorship and training to laboratory personnel, trainees, and collaborators in computational biology and multi-omics data analyses.
Supports capacity building within the laboratory by developing analytical standards, protocols, and educational resources.
Fosters an inclusive, interdisciplinary culture of collaboration and promotes effective knowledge sharing across diverse research teams.
Represents the laboratory in high-level collaborative meetings, international workshops, and scientific working groups.
Ensures all computational and data storage activities strictly comply with institutional policies, ethical standards, privacy regulations, and data-sharing agreements.
Maintains current knowledge of relevant scientific, technical, and regulatory developments in computational biology and AI in precision health.
Performs other senior-level duties consistent with the qualifications and classification of the position.