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the application of these methods to problems in the physics of oxides, semiconductors, metals and their surfaces. Machine learning methods are used to close the complexity gap. Currently, the group consists
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quantitative field. Strong background and expertise in data science, bioinformatics, network science, artificial intelligence, machine learning, deep learning, or related areas. Solid understanding of AI
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strengths in experimental soft condensed matter physics or biophysics research within the department. Candidates with expertise in computational physics, including machine learning, applied to study soft
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 3 days ago
computer package will be used. Course Enrolment (Estimated): 120 Number of Positions: 1 TA Support: 50 hrs per tutorial & per semester Sessional Dates of Appointment: July 1, 2026 – Aug 31, 2026 Class
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security, cryptography, privacy-preserving and trustworthy computing, AI-driven security and adversarial machine learning, security of cyber-physical and autonomous systems, and/or cloud or edge system
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directed by Specialist and postdoctoral fellows. The final salary and offer components are subject to additional approvals based on UC policy. Your placement within the salary range is dependent on a number
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snails; (2) assist in developing computational digital twin and machine learning simulations; (3) build and test robotic prototypes; and (4) undertake developmental work including data analysis and paper
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learning techniques, and on the development of integrated motion-corrected analysis of positron emission tomography (PET)/computed tomography (CT) angiography imaging. As a Postdoctoral Scientist, you will
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learning techniques, and on the development of integrated motion-corrected analysis of positron emission tomography (PET)/computed tomography (CT) angiography imaging. As a Postdoctoral Scientist, you will
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that incorporate artificial intelligence and machine learning or climate change and human health are of particular interest. BWF believes that a diverse scientific workforce is essential to the process and