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Field
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’ or ‘internationally excellent’. The highly research active SP Section comprises 13 permanent academic staff with research interests in Bayesian computational statistics and machine learning, uncertainty quantification
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, R) Expertise in machine learning, Bayesian statistics is beneficial Capacity for interdisciplinary teamwork and excellent communication skills Ability to communicate in English fluently
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disease data. A collaborative mindset, excellent communication skills, and the ability to work across disciplines. (Desirable) Familiarity with malaria epidemiology, Bayesian methods, or international
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more novel problems. Keywords include: automatic experimental design, Bayesian inference, human-in-the-loop learning, machine teaching, privacy-preserving learning, reinforcement learning, inverse
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, mathematical psychology (computational modelling) and/or human factors methods and related statistical techniques (including Bayesian hierarchical methods) Experience with the development and application
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. Experience in the implementation of mathematical or statistical models and model fitting, including Bayesian model fitting, is desirable but not essential. Familiarity or experience of management and analysis
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- based neural networks, Bayesian statistics, and text analytics are a must. Nice to Have: Experience developing and integrating APIs for healthcare systems to ensure seamless interaction with AI models
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and Bayesian methods. Knowledge of statistical software, particularly R. Strong statistical programming skills. Understanding of clinical trials. An ability to work well both on own initiative and
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behaviour using computational approaches such as Bayesian program synthesis and inverse reinforcement learning. Investigate the diversity of motor commands that could implement observed behaviours and explore
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interpreted very broadly, e.g.: topics in Bayesian Inference and Robotics; ‘Science’ covers any typical topic in Natural Science and Engineering (Epidemiology, Biology and basic science in biomedicine