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research in cardiovascular and autonomic (i.e., bowel, bladder, sexual and cardiovascular) dysfunctions following SCI Demonstrated expertise in current machine learning techniques applied to biological
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PhD (or equivalent) in Machine Learning, Computer Science/Engineering, Biomedical Engineering, or PhD or equivalent degree in population/public health and other related medical fields with AI and ML
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of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and
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Engineering Research Associate: Advanced Renewables At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning
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projects and deliverables May supervise undergraduate students working on the AI/ML projects QUALIFICATIONS PhD (or equivalent) in Machine Learning, Computer Science/Engineering, Biomedical Engineering, or
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clinical trials, advanced computational methods, neuroimaging, brain stimulation, body-machine interfacing, gamification of therapy, assistive technology design, development and evaluation, outcome measure
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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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junior research team members (including undergraduate research trainees). Qualifications ● PhD in computer science, bioinformatics, data science or similar, with a focus on machine learning
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experimental design. Proficiency with machine vision and deep learning techniques, including image segmentation, landmark placement and metric learning, for the automation of phenotypic analysis of large image
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 13 hours ago
, although candidates are expected to have established strengths in spatial statistics and/or applied machine learning. Grounded in the CERC-NEST goal of connecting statistics with knowledge co-production