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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Computational Pathology, Medical Imaging, and Clinical Text Analysis Department Bashashati
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Genomics, Transcriptomics, and Bioinformatics Department Bashashati Laboratory | School
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The University of British Columbia (UBC) | Vancouver UBC, British Columbia | Canada | about 9 hours ago
in Machine Learning for Computational Pathology, Medical Imaging, and Clinical Text Analysis Job Summary The School of Biomedical Engineering at the University of British Columbia, Vancouver campus is
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The University of British Columbia (UBC) | Vancouver UBC, British Columbia | Canada | about 9 hours ago
team in the AI in Medicine Lab (www.aimlab.ca ). This position is based in the School of Biomedical Engineering. The successful candidate will work in the AI in Medicine Lab, applying machine learning
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expertise in artificial intelligence (AI), machine learning (ML), and data science. The position will be a part of the Walk Tall research team based at BC Children’s Hospital. The Postdoctoral Fellow will
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machine learning with single-cell genomics, spatial omics, and systems biology, supported by strong collaborations across UBC and internationally. Project Recent advances in single-cell and spatial omics
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standards across the various academic divisions, as well as supporting postdoctoral fellows. SGS defines and administers University-wide regulations for graduate education. We share responsibility
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Successful applicants will have: PhD and postdoctoral fellow training with expertise in cancer biology, proteomics and/or computational biology / machine learning A proven track record with first author
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Academic Job Category Faculty Non Bargaining Job Title Postdoctoral Research Fellow in Machine Learning for Genomics, Transcriptomics, and Bioinformatics Department Bashashati Laboratory | School
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Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a closely related technical field. Demonstrated knowledge of or interest in Indigenous Knowledge Systems and interest in applying IKS