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pharmacy. In close collaboration with partners from industry, healthcare, and society, we contribute to the urgent challenges of our time, such as energy, sustainability, digitization, and medical technology
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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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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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. 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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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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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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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