University Health Network
Biomedical Research
PostdoctoralResearcher
“Postdoctoral Researcher at University Health Network. Skills: computational biology, statistical modeling, analysis of high-dimensional biological and clinical data, reproducible computational pipelines, translational cancer research, machine learning approaches, biomarker discovery, therapeutic response modeling, clinical translation, multi-omics data integration. Design, implement, and maintain reproducible computational pipelines for the analysis and integration of multi-omics and clinical da”
What You'll Achieve.
advance translational cancer research through the development and application of computational pipelines and machine learning approaches to characterize disease biology, therapeutic response, and clinical outcomes from high-throughput genomic and clinical data
Industry & Context.
Criminal Record Check may be required
What They're Looking For.
Must Have
PhD within the previous 5 years, or an MD or DDS within the previous 10 years in a relevant quantitative or biomedical discipline, Demonstrated experience developing or applying computational or statistical pipelines to molecular, biological, clinical, or multi-omics data, foundation in statistical and computational modeling and data analysis, programming skills in Python and/or R, with experience in scientific computing and data analysis libraries (e. g. , pandas, NumPy, SciPy, Bioconductor, scikit-learn), Experience working with large-scale biomedical datasets, such as multi-omics data, clinical genomics, electronic health record-derived data, or treatment-response datasets, Proficiency with reproducible workflow management systems such as Snakemake, Nextflow, or equivalent pipeline frameworks, publication record, commensurate with career stage, in computational biology, bioinformatics, biostatistics, biomedical data science, or related fields
Nice to Have
experience with machine learning methods is considered an asset, Understanding of data harmonization, privacy-preserving analysis, secure distributed computing, or clinical data governance is highly desirable
What You'll Do.
and maintain reproducible computational pipelines for the analysis and integration of multi-omics and clinical datasets
Develop and apply statistical methods and computational workflows for biomarker discovery
drug response characterization
and tumour microenvironment analysis
Perform data curation
and quality control across public and institutional datasets
following FAIR data principles
Develop and apply machine learning methods alongside statistical approaches to support biomarker discovery and clinical prediction
How You'll Work.
Team & Collaboration
work collaboratively in interdisciplinary teams spanning computational biology, machine learning, software engineering, oncology, and clinical research
Communication Scope
Excellent communication skills
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