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aims to optimize the operations (serving) of AI by developing algorithms that manage compute, network, and storage resources in a carbon-efficient way while supporting long-term benefits
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research in neuro-symbolic AI, with a focus on using generative AI and prompt engineering as a method to engineer knowledge graphs one can trust. This includes the design of algorithms and architectures, but
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small highly motivated inter-disciplinary team working towards a shared goal. You will be responsible for the design and testing of original machine-learning based algorithms and models for multi-modal
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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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to work on meaningful projects with direct clinical relevance. About the role In this role, you will develop and implement computer vision and deep learning algorithms to analyse CT and MRI data from
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developing new algorithmic approaches for TAPS data, interpreting the results in the context of phenotypic observations, and communicating these findings clearly to the broader team. You will prepare the
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developed goal-sequence generalization task. The project will integrate high-density silicon probe recordings, optogenetics, pharmacology and advanced computational tools to analyse neural algorithms
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type (iv) work with the computational biology team to transfer this information into a AI algorithm that can distinguish neurodegenerative and neuroprotective phenotypes (v) work with colleagues in
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developing cutting-edge research on all aspects of computational imaging, from theory and algorithms, to applications in astronomy and medicine. Dr Wiaux is a Professor in the School of Engineering and
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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