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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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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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data and archaeological signatures (e.g., circular mounds, linear ditches, rectangular foundations, etc.) tailored for AI applications – Feature engineering and representation learning to enhance
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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
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collaborative research projects, including but not limited to: • Developing and applying bioinformatics, pharmacogenomics, AI, machine learning, and deep learning methods • Analyzing and integrating large-scale
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students to analysing multimodal learning data (e.g., individual as well as collaborative verbal interactions, student gestures, task and activity sequences) and evaluating long-term learning outcomes. As
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capacities for dialogue, disagreement, collaboration, and peaceful conflict engagement in diverse and often polarized environments. A central component of the PhD project will be the comparative mapping
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Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
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, as well as learn how to perform robust in silico analysis of data (training will be provided by senior lab members, collaborators, or experts in the Institute core facilities). The candidate will