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on the mathematical theory of deep learning as part of the joint NSF-EPSRC project “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
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advance the fundamental science of artificial intelligence and address some of the field's most important challenges. You will be responsible for researching and developing novel algorithms and techniques
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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Paul Goldberg and will work on the EPSRC-funded project “Driving Behaviour in Multi-winner Voting” within the Algorithms and Complexity Theory research theme. The position is for the two-month period of
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tag and system architecture, including: power harvesting and management; data collection approach; LTE antenna integration in an extremely small footprint; message format and data compression
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deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will
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, Statistics, Computer Science or conjugate subject and have a strong record of publication in the relevant literature. Good knowledge of machine learning algorithms is essential, as well as proven competence in
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of Oxford. The post is funded by United Kingdom Research and Innovation (UKRI) as part of the ACORN IAA and is for 12 months. The researcher will develop visual mapping and change detection algorithms
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research and implementation. You will also develop novel algorithms using state-of-the-art computer vision and machine learning techniques (segmentation, multimodal AI, foundation models, agentic frameworks