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explores novel aggregation methods at the intersection of AI safety, computational social choice, and judgment aggregation, aiming to formally integrate multi-stakeholder preferences into AI system design
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Automated Verification theme in the Department of Computer Science and a research group with responsibility for carrying out research on the robustness (continuity) of equivalences in probabilistic systems
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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We are seeking a creative and highly motivated postdoctoral researcher to join the Turing AI World-Leading Fellowship research programme led by Professor Alison Noble. This exciting and ambitious
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Computational Methods for Advanced Research to Transform Biomedicine ( SMARTbiomed ), an international collaboration that integrates large-scale, multimodal biomedical data with advances in statistical and
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position is part of the AI2 (Algorithmic Assurance and Insurance) research initiative, an ambitious programme supported by the UK Prosperity Research Scheme with partners from both industry and academia. Our
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team. You will lead in the design and implementation of statistical and computational algorithms of different datasets, and implement novel algorithms within the framework of existing code, providing
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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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recordings, optogenetics, pharmacology and advanced computational tools to analyse neural algorithms, their deficits and their rescue in genetic mouse models. This project is part of a cross-species, cross
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collaboration with the Translational Gastroenterology Unit (TGU) and the Ludwig Institute of Cancer Research (LICR) we aim to develop a computer guided endoscopy image recognition system that will support