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and beyond worst-case analysis, and developing new theoretical frameworks connecting quantum algorithms, learning theory, metacomplexity, and post-quantum cryptography. The postholder will play a key
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development of universal techniques for designing learning-augmented algorithms. About Us The University of Oxford is a stimulating work environment, which enjoys an international reputation as a world-class
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capable of supporting and collaborating with humans in complex, real-world settings. You will be responsible for researching and developing novel algorithms and techniques to achieve the project’s
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statistics, AI, and computation to generate biological and medical insight. The group focuses on the development of novel algorithms, tools, and databases that are open source and freely available to all users
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preferences into AI system design and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute
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are twofold. The first goal is to accelerate the solution of the large mixed-integer optimisation problems required to balance energy. The second goal is to develop methods that handle the increasing
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variables against expert judgement to determine which variables should enter causal estimation as confounders, and evaluate competing causal discovery algorithms across multiple datasets. Third, it will task
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role in tackling many global challenges, from reducing our carbon emissions to developing vaccines during a pandemic. The Department of Computer Science at Oxford is renowned for pioneering research and
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the classification of health outcome data in the UK Biobank resource. You will be responsible for developing a programme of work to enhance the phenotyping of health outcomes in UK Biobank, investigate new data
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chemistry platform that accelerates fragment progression and synthesis. You will also integrate and refine computational tools developed by the XChem Fast Forward Fragments team at Diamond Light Source