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to projects involving: multi-omics analyses (single-cell, cell-free RNA/DNA, methylation, proteomics); biomarker discovery for pregnancy complications and neonatal outcomes; computational biology, machine
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biology, machine learning, and predictive modelling; translational studies integrating laboratory discoveries with clinical implementation. Vancouver is a vibrant coastal city located on the traditional
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supervising of trainees Providing other support as required by the research team The PDF will be supervised by Dr. Matthew Mitchell, who leads the Mitchell Multifunctional Landscape Lab (https
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University of British Columbia Department of Surgery | Northern British Columbia Fort Nelson, British Columbia | Canada | 2 months ago
biology; Translational immunology Treg biology Excellent organization skills, along with an outstanding level of accuracy and attention to detail. Computer experience required (word processing, spreadsheet
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advanced biostatistics Computer proficiency, including use of statistical software for spatial analyses, such as ArcGIS, QGIS, R, Google Earth Engine Experience assembling, organizing, and analysing large
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systems Working knowledge of ecological (especially landscape ecology) research methodology and advanced biostatistics Computer proficiency, including use of statistical software for spatial analyses
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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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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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, 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
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, interventional effects, multiple mediation), and advanced longitudinal modelling (mixed-effects models, growth curve models, latent class trajectories) with machine learning and AI-based approaches. The Lab is