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Field
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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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About the Role We are seeking brilliant and passionate Algorithm Researchers to join our core team dedicated to advancing the frontiers of Artificial General Intelligence (AGI). In this role, you
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of sparse matrix, tensor and graph algorithms on distributed and heterogenouscomputational environments. Basic Qualifications: A PhD in Computer Science, Applied Mathematics, Computational Science, or related
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literature. Foundational Algorithms for AGI AGI will not emerge from scaling existing models alone; it requires a new algorithmic foundation for learning, reasoning, and adaptation. This research area is
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, encryption/decryption and compression; use of microelectronics devices (including COTS); implementation, inference, verification and validation of algorithms** on processing hardware platforms for space
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developing new methods and techniques that will improve standard ML algorithms so as to achieve good performance outside their training distribution, by treating high-dimensional problems as an explicit
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physical laws, or an implicit form of extra data examples collected from physical simulations or their ML surrogates. In medical domains, patient data is typically distributed across multiple hospitals
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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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and its implementation in distributed systems. Main responsibilities: Research, develop, and optimise machine learning algorithms, including deep learning, for AV control and coordination. Apply multi
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, the post-doctoral fellow will consider designing distributed learning algorithms for streaming manifold-valued data. Experiments will be carried out on urban, coastal, and underwater DAS data. The novelty