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Vision Group (PVG) at McGill University and Mila—Quebec Artificial Intelligence Institute, led by Prof. Tal Arbel, seeks Postdoctoral Fellows to advance causal-temporal and probabilistic modeling, 3D
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explored. These strategies will be evaluated on cellular and mouse disease models, in order to determine whether APP editing can reduce pathogenic effects caused by amyloid-beta. In addition to gene editing
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from correcting disease-causing mutations, the potential of introducing protective mutations will be explored. These strategies will be evaluated on cellular and mouse disease models, in order to
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process design, computational modeling, and techno-economic analysis. The start date for the position is 1 October 2025, or shortly thereafter. The salary for the position is $50,000 per year. We encourage
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to understand the needs, experiences and challenges of people with rare musculosleketal conditions. Conduct quantitative analysis on functional outcomes for descriptive and modelling purposes. Partner with
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with PERL’s mission Our Science & Evidence learning tracks: Pandemic prevention & risk anticipation (e.g. primary and secondary prevention, surveillance innovation, spillover risk modeling, zoonoses
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allows for translation of novel methods to our cancer clinics in Canada (Vancouver, Victoria, and Kelowna) and beyond. Requirements: The ideal candidate will have a PhD in computational modeling/oncology
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Grant (ORF-RE (external link) ). The SSHRC-funded project was launched in 2021 and the objectives of this research are to: 1) expand conceptual models for intergenerational partnerships; 2) investigate
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 2 months ago
. For those interested, there is also an option to learn and use in vivo cancer or diabetes models. Collaborative and Supportive Environment: You will collaborate closely with experienced scientists and
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approaches (based on functional programming abstractions) to optimize the implementation of machine learning models and other digital signal processing algorithms on a specific FPGA architecture to fit within