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embrace the complexity of digital health implementation and data-driven predictive modelling in low-resource settings with passion, resilience, and lots of creativity. Find yourself in new areas of inquiry
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research proposals and implementing data collection and analyses. Share findings with health system and community partners (through meetings, summary briefs, presentations), present at public health and
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discovery challenges. Qualifications: PhD in bioengineering, computational biology, machine learning, systems immunology, or related discipline, obtained within the last 5 years, by the time of
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implementing data collection and analyses. Share findings with health system and community partners (through meetings, summary briefs, presentations), present at public health and academic conferences and
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germplasm data to ensure genetic composition and genetic diversity of bison subspecies. Nature of Work: We are looking for energetic and productive postdoctoral fellow who possesses laboratory skills related
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The fellow will be responsible for: Building collaborations with our multidisciplinary team (medical physicists, engineers, computer scientists, nuclear medicine physicians) to develop and implement innovative
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The DEPRESsion Screening Data (DEPRESSD) Project is a collaborative endeavor, set up by Drs. Brett Thombs and Andrea Benedetti, to evaluate the diagnostic accuracy of depression screening tools and
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Canada (Vancouver, Victoria, and Kelowna) and beyond. RESPONSIBILITIES The fellow will be responsible for: Building collaborations with our multidisciplinary team (medical physicists, engineers, computer
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transportation operations and network modelling, accessibility analysis, data analysis (statistics and/or machine learning methods), and spatial mapping. Because the work will involve multiple years of daily
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vitro and in vivo experiments using advanced molecular and cellular techniques, ● Analyze and interpret data from preclinical models and human samples to identify potential therapeutic targets, ● Publish