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consumer demand for plant-based foods and animal welfare. These projects rely on large-scale datasets and demand strong skills in data management and statistical modelling. Additional expertise in behavioral
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successful candidate will work in the AI in Medicine Lab, applying machine learning to cutting-edge problems in cancer genomics, transcriptomics, spatial transcriptomics, and large-scale bioinformatics
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findings of bibliometric, quantitative, and qualitative data Assist in the preparation of grant reports Qualifications: Must be a registered McGill student Experience with large, structured literature
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evidence from diverse sources, analyze international survey data, and contribute to the development of a conceptual framework that captures the multi-level and intersectional nature of stigma. Findings from
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to write and communicate clearly Preferred Qualifications Experience with high performance computer clusters Experience with the analysis of large datasets Good understanding of immunology Any kind
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(quantitative and qualitative). They must also have experience working with large datasets and producing high-quality data visualizations and linking agronomic and genetic factors. Experience in Python/R would be
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, collects, and analyzes relevant information and injury data relating to current projects. Conducts systematic reviews including but not limited to: title and abstract screening, full-text review, quality
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, collects, and analyzes relevant information and injury data relating to current projects. Conducts systematic reviews including but not limited to: title and abstract screening, full-text review, quality
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and effective anti-tumour immune responses; and 3) develop therapeutic strategies to promote these beneficial immune states. Position Overview The Postdoctoral Fellow will lead multi-omic data
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, analysis, and integration of large multi-omic and imaging datasets. Support collaborative projects by coordinating data sharing, documentation, and reproducible workflows. Assist in preparing manuscripts