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of medical science, and educators to advance learning. We are proud to be part of progress, working together with the communities we serve to share knowledge and bring greater understanding to the world
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excellence within the School of Engineering, with a strong focus on Computational Geomechanics and industry-engaged research translation. This role is suited to a senior academic who combines deep technical
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university, Waterloo combines multi-disciplinary research, deep connections with industry, unique creator-owned IP policy and robust commercialization support to turn discoveries into real-world solutions
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of the future, striving to improve healthcare and society as a whole. Where to apply Website https://www.academictransfer.com/en/jobs/356668/phd-in-machine-learning-for-dru… Requirements Specific Requirements
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to understand the underlying mechanisms and develop predictive capabilities. The project aims to apply deep learning and artificial intelligence to make predictions using imperfect data and partial knowledge
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, GNSS positioning is highly susceptible to errors from atmospheric distortions, multipath effects, and receiver noise. Recent advances in deep learning have shown that data-driven pseudorange correction
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is expected to have a profound knowledge on most of the following topics: Robot control Deep Learning Medical imaging Preferably, the candidate has experience with: Robotic simulation tools Medical
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; a global network of campuses and partners for students and faculty to leverage for learning and research; a deep investment in lifelong and experiential learning; a premium placed on pedagogical
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) a PhD in a quantitative discipline such as computer science, mathematics, statistics, engineering, or a related field. Strong programming skills and experience in machine learning or statistical
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leadership and specialist advice, particularly in fostering outstanding research and/or learning and teaching within UTAS and Department of Health. What we’re looking for: A PhD or equivalent in a relevant