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and grippers offer improved safety and adaptability but introduce new challenges in design and control. Their development is still largely bio-inspired and trial-and-error based. Integrating flight and
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Deadline: 30 June 2025 Details This project aims to develop new algorithms for reinforcement learning from human feedback, to effectively solve complex reinforcement learning tasks without a predefined
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dynamics, solid mechanics, soft matter or active matter. • To become familiar with simulation algorithms as needed, assist in the development of new ones, test and document any newly developed
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(AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of
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(AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of
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(AC) at the University of Toronto (U of T) is leading a transformative shift in scientific discovery that will accelerate technology development and commercialization. The AC is a global community of
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Numerical Simulations of Complex Dynamic Systems, E.g. Turbulence, Hurricane. The duty of the role is within the range of: 1) developing novel algorithms on data analyses and data mining, e.g. Large Models
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(networked) dynamical systems" (broadly defined) and there is ample room for exploring sub-topics depending on the interest of the candidate. The focus will be on deriving efficient algorithms with provable
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you will do Driving innovative AI research through the development and implementation, practical application, theoretical analysis and evaluation of AI algorithms Use of XAI tools to explain machine
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foundation models, multi-modal learning algorithms, generative models, and large language models [1], have made seen remarkable advancements in the field of healthcare. It can lead to more accurate diagnosis