Classification
Machine Learning and Data Science
Research Themes
* Learning with Structured & Geometric Models – apply tools from manifold learning and Riemannian optimisation to better training and novel network designs.
* Low Effective-dimensional Learning Models – extend foundational theory to reduce trainable parameters without sacrificing accuracy, analysing low‑dimensional building blocks.
* Implicit Regularization – develop mathematical understanding of implicit regularisation in deep neural networks to guide algorithmic paradigms that combine statistical optimality and computational efficiency.
* Reinforcement Learning through Stochastic Control – develop stochastic control methods providing mathematically grounded continuous‑time RL, addressing scalability for high‑frequency observations.
Position Details
This is a two‑year fixed‑term position funded by a research grant from the EPSRC. The start date is flexible.
Responsibilities
* Conduct research within the remit of the large‑scale project, collaborating with other hub members at Oxford and partner institutions.
* Publish results with co‑authors in refereed journals and proceedings.
* Contribute a small amount of teaching (at most three hours a week during academic terms).
Application Requirements
Applicants must provide two referees who will send reference letters directly to references@maths.ox.ac.uk before the closing date.
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