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will be able to demonstrate: • A PhD in the Mathematical Sciences or in a closely related field, or equivalent. • A strong research record with publications of outstanding quality. • A clear plan to
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to curriculum design, innovation, and enhancement. You will play a key role in fostering an inclusive, engaging, and high‑quality student experience, and will support the academic and professional development
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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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or in a closely related field, or equivalent. • A strong research record with publications of outstanding quality. • A clear plan to build or further develop an internationally leading research programme
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. Additional benefits including enhanced holiday pay, life assurance, income protection, private medical insurance, hospital cash plan, therapy services, perk box. Terms of Appointment: You must be eligible
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Moss to: Develop machine learning emulators for the WAVI ice-sheet model to serve as efficient surrogates for large-scale Bayesian inference. Develop utility-function-based experimental design methods
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. This post is funded by an EPSRC Grant `Sharifi conjecture for Bianchi 3-manifolds’ held by Dr Tobias Berger and is fixed term for 36 months. The project is developing new tools in the deformation theory
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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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, distributed training, cloud compute, or cluster environments; have experience with large language models, either through training, fine-tuning, evaluation, inference, tooling, or deployment; are able to design
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course development, dissertation and project supervision. The post sits within the School’s Statistics Group, part of the wider Data & Decisions Theme, which is active in modern mathematical and