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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow, Machine Learning Department Cooper Laboratory | Department of Orthopaedics | Faculty of Medicine (Anthony Cooper
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. The research will focus on artificial intelligence, machine learning, data analytics, and recommendation systems, with applications to personalized decision-making, digital platforms, digital health, and mental
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an industry partner. Experience with research software, data pipelines, and simulations, machine learning, high-performance computing, CANFAR, or advanced data systems. Evidence of mentoring or supervising
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connections to their communities and you may be eligible for an exception to this work arrangement. Alternative work arrangements may also be considered to accommodate candidates as required. To learn more
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multivariate, statistical-genetic or machine-learning approaches. · Research involving developmental, ageing or neuropsychiatric cohorts, including longitudinal or large-scale population datasets. · High
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. · Knowledge of molecular biology, cell biology, immunology, neuroscience, or ophthalmic research is considered an asset. · Experience with single-cell genomics, spatial transcriptomics, machine learning
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, or ophthalmic research is considered an asset. · Experience with single-cell genomics, spatial transcriptomics, machine learning, artificial intelligence, or translational biomedical research is considered
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, Bioconductor, tidyverse, SingleCellExperiment, or related software. Experience with Python and machine learning approaches is considered an asset. APPLICATION PROCEDURE Applicants should submit: Cover letter
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: The R2L Lab explores how language understanding improves machine learning efficiency and generalization. We are a leader in agentic benchmarks and evaluation; our platforms serve as primary evaluation
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, including parsing and processing large document corpora. Strong understanding of machine learning or AI methods applied to health or biomedical data. Demonstrated ability to assess model outputs, identify