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Postdoctoral Research Associate in the Mathematical and Computational Foundations of Artificial Intelligence
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application opening date: 2024-10-24T09:00:00+01:00
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comment: We invite applications for four Postdoctoral Research Associates (PDRA) to join the EPSRC Hub on the Mathematical and Computational Foundations of Artificial Intelligence. One PDRA will be recruited for each of the following four research themes: Learning with Structured & Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning through Stochastic Control. A brief description of each these is as follows (additional details are in the further particulars): **Learning with Structured and Geometric Models.** We will apply tools from manifold learning and Riemannian optimisation to leverage the underlying manifold structure for better training and novel network designs. **Low Effective-dimensional Learning Models.** We will extend foundational theory of how large ML systems can be regularised to have dramatically fewer trainable parameters without sacrificing accuracy by analysing the use of low- dimensional building blocks **Implicit Regularization.** We aim to develop mathematical understanding of implicit regularisation properties in deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. **Reinforcement Learning through Stochastic Control.** We will develop methods from stochastic control, which will provide a mathematically grounded approach that has a well-posed continuous-time limit (as opposed to traditional RL methods that are inherently discrete and do not scale favourably for high frequency observations without judicious hyper-parameter tuning). The PDRAs will work with faculty across the multi-university Hub, but will be employed by and directly supervised by faculty within the Mathematical Institute at the University of Oxford. Faculty within the Mathematical Institute associated with the above work packages include Profs. Cartis, Cohen, Hauser, Lambiotte, Reisinger, Sirignano, and Tanner. These are two-year, fixed-…
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comment: We invite applications for four Postdoctoral Research Associates (PDRA) to join the EPSRC Hub on the Mathematical and Computational Foundations of Artificial Intelligence. One PDRA will be recruited for each of the following four research themes: Learning with Structured & Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning through Stochastic Control. A brief description of each these is as follows (additional details are in the further particulars): Learning with Structured and Geometric Models. We will apply tools from manifold learning and Riemannian optimisation to leverage the underlying manifold structure for better training and novel network designs. Low Effective-dimensional Learning Models. We will extend foundational theory of how large ML systems can be regularised to have dramatically fewer trainable parameters without sacrificing accuracy by analysing the use of low-dimensional building blocks Implicit Regularization. We aim to develop mathematical understanding of implicit regularisation properties in deep neural networks to guide the development of algorithmic paradigms aimed at combining statistical optimality with computational efficiency. Reinforcement Learning through Stochastic Control. We will develop methods from stochastic control, which will provide a mathematically grounded approach that has a well-posed continuous-time limit (as opposed to traditional RL methods that are inherently discrete and do not scale favourably for high frequency observations without judicious hyper-parameter tuning). The PDRAs will work with faculty across the multi-university Hub, but will be employed by and directly supervised by faculty within the Mathematical Institute at the University of Oxford. Faculty within the Mathematical Institute associated with the above work packages include Profs. Cartis, Cohen, Hauser, Lambiotte, Reisinger, Sirignano, and Tanner. These are two-year, fixed-term position, fu…

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value: MATHEMATICAL INSTITUTE ANDREW WILES BUILDING Job Description and Selection Criteria Job title Postdoctoral Research Associate in the Mathematical and Computational Foundations of Artificial Intelligence Division Mathematical, Physical and Life Sciences Department Mathematical Institute Location Andrew Wiles Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG. Grade and salary Grade 7: £40,855 - £46,913 p.a. Hours Full time (40 hours / 100%FTE) Contract type Fixed-term (2 years) / externally-funded Reporting to Prof. Cartis, Lambiotte, Reisinger, and Tanner. Vacancy reference 176180 This is a full-time position that cannot be held concurrently with any other substantive post without the explicit permission of the Head of Department Additional information This post is subject to a 12-month probationary period. This post is funded by EPSRC. (PLEASE NOTE: Applicants are responsible for contacting their referees and making sure that their letters are received by the closing date) Research topic Mathematical Intelligence and Computational Foundations of Artificial Principal Investigator Prof. Cartis, Lambiotte, Reisinger, and Tanner. / supervisor Project team Profs. Cartis, Cohen, Hauser, Lambiotte, Reisinger, Sirignano, and Tanner. Project web site www.ox.ac.uk/groups/ml-and-ds Funding partner The funds supporting this research project are provided by EPSRC grant EP/Y028872/1 “Mathematical Foundations of Intelligence: An “Erlangen Programme” for AI. The role We invite applications for four Postdoctoral Research Associate (PDRA) to join the EPSRC Hub on the Mathematical and Computational Foundations of Artificial Intelligence. One PDRA will be recruited for each of the following four research themes: Learning with Structured & Geometric Models, Low Effective-dimensional Learning Models, Implicit Regularization, and Reinforcement Learning through Stochastic Control. The PDRAs will work with faculty across the multi-university Hub, but will be employed …

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