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Simon Fraser University | Northern British Columbia Fort Nelson, British Columbia | Canada | about 2 months ago
at all levels—developing new materials, designing creative and interactive technologies, engineering future hardware platforms like quantum computers, and writing the algorithms that power machine learning
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collaboration with industry partners. This work will apply optimal control theory, including machine-learning algorithms and Bayesian estimation, to coherent control of nitrogen-vacancy centers in diamond
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J1K2R1, Canada [map ] Subject Areas: Theoretical Physics / Quantum Optics Quantum Information Science Appl Deadline: 2025/09/01 11:59PM (posted 2024/08/29, listed until 2025/09/01) Position Description
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, Quebec J1K 2R1, Canada [map ] Subject Areas: Theoretical Physics / Quantum Optics Quantum Information Science Appl Deadline: 2025/09/01 11:59PM (posted 2024/08/29, listed until 2025/09/01) Position
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currently do not hold a TAship or do not have a profile in Workday: please apply externally using an external email address (do not use your @mail.mcgill.ca). Hiring Unit: School of Computer Science Course
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currently do not hold a TAship or do not have a profile in Workday: please apply externally using an external email address (do not use your @mail.mcgill.ca). Hiring Unit: School of Computer Science Course
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work independently on complex projects. Experience and Education Master’s degree in Software and Computer Engineering (French engineering schools are preferred). Experience in optimizing ML algorithms
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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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scientists, nuclear medicine physicians) to develop and implement innovative AI algorithms applied to medical images To lead effort on enabling translational and physician-in-the-loop AI solutions for medical
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Engineering Department: Electrical & Computer Engineering Campus: St. George Contact Email Address: ugta.ece@utoronto.ca Course Number and Title: APS105H1 S – Computer Fundamentals Course Description: An