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developing learning-based perceptive approaches. You will develop learning and/or optimization based approaches to multi-robot path and motion planning and coordination, integrating multi-modal perception and
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projects. Our recent work has focused on the theory and applications of quantum walk algorithms, variational quantum algorithms and their optimization, adiabatic quantum computation, and quantum machine
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of advances in the field. This new version will not be backwards compatible with v1, to allow for unconstrained optimal design. None of the above includes modelling of longitudinal data. The timepoint2 datasets
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multiple ship classes and shore power applications. The project seeks to deliver industry-relevant modelling toolkits that enable optimal design and operation of greener vessels, backed by real-world
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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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Research Assistant or Postdoctoral Research Associate About the Role The School of Electronic Engineering and Computer Science at Queen Mary University of London seeks to recruit a Research
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multiple ship classes and shore power applications. The project seeks to deliver industry-relevant modelling toolkits that enable optimal design and operation of greener vessels, backed by real-world
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optimization of large language models (LLMs) and related architectures for generative tasks, continuous learning, indexing or retrieval, support of retrieval augmented generation over many data points from long
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optimization; physical tag design compatible with its installation requirements (the leg of a bird); prototype tag building; and development of a simple server side system for data aggregation. Documenting
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modelling toolkits that enable optimal design and operation of greener vessels, backed by real-world demonstrations. The successful candidate will work at the intersection of multi-disciplinary modelling