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Investigator. Strong knowledge of qualitative research methodologies and familiarity with participatory research approaches are an asset. During your PhD, you will collaborate with the PI and a postdoctoral
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unsupervised learning. Research Environment & Collaboration The successful candidate will work at the interface of probabilistic machine learning, computational statistics, and biostatistics, developing new
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. The ability to communicate scientific work clearly in spoken and written English. The ability to work both independently and collaboratively in field, laboratory and multidisciplinary research settings
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or quantitative data analysis is a plus. You are interested in developing and applying novel technologies and are willing to learn and work across disciplinary boundaries. You collaborate with
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function. Strong organizational and communication skills. Ability to work both independently and collaboratively. Motivation to learn new experimental approaches and progressively develop an independent
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facilities and strong interdisciplinary collaboration. You will work alongside researchers from a wide range of disciplines and engage with hospitals, industry and public-sector partners to address some of
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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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digital learning environments combined with repeated survey measures. A willingness to engage in interdisciplinary collaboration and teamwork. Proficiency in English as the working language of
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independently while being part of a collaborative team Our offer 🎓Learning & Growth: Take advantage of seminars, trainings and language courses to expand your skills 📋Structured Onboarding: Benefit from a
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. To learn more about our work environment, colleagues, leaders, students and innovative educational environment, visit www.torontomu.ca , check out @TorontoMet (external link, opens in new window) and