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research will develop and apply novel Bayesian machine learning methods – in particular physics-informed Gaussian processes and/or neural operators– to build accurate probability density functions (PDFs
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upskill on algorithmic information theory, AIXI, Bayesian statistics, and reinforcement learning theory (existing expertise on these topics not required). Proven ability to independently design, build, and
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their cover letter how their research and teaching aligns with the Joint Mathematics and Computing degree. For further information, please contact the head of the section closest to your area of research
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and informal enquiries about this post may be directed to the Head of School, Professor Antonia Wilmot-Smith ([email protected]) or the Head of Pure Mathematics, Professor Colva Roney-Dougal
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and Computing degree. For further information, please contact the head of the section closest to your area of research: Professor Paolo Cascini (Pure Mathematics, [email protected]), Professor