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Postdoctoral Research Associate in Deep Learning
salary: Standard Grade 7: £38,205 - £44,263 per annum
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comment: We are currently inviting applications for a Postdoctoral Research Associate to work with Professor Justin Sirignano at the Mathematical Institute, University of Oxford. This is a three-year, fixed-term position, funded by a research grant from the Engineering and Physical Sciences Research Council (EPSRC). The starting date of this position is flexible with an earliest start date of 01 March 2024. We particularly welcome applications from individuals who are able to start between 01 March and 01 October 2024. The successful candidate will be part of a research group funded by a joint NSF and EPSRC grant to the University of Oxford and the Boston University: “DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks”. The research will involve collaboration between the postdoctoral research associate, Prof. Justin Sirignano (University of Oxford), and Prof. Konstantinos Spiliopoulos (Boston University). Neural network models in machine learning have achieved immense practical success over the past decade, revolutionizing fields such as image, text, and speech recognition. The training algorithms used for these complex machine learning problems – although successful in practice – are often ad hoc. Mathematical theory is yet to be established in many cases, and there is the potential to improve training algorithms and models via rigorous mathematical analysis. The objective of this research project is to develop new mathematical theory for the training algorithms and neural network models used in several key areas of machine learning. Analysis will leverage methods from stochastic analysis and weak convergence theory to study the asymptotics of online, stochastic training algorithms and neural network models as the number of hidden units becomes large. The research project is highly interdisciplinary, integrating methods from probability, partial differential equations, stochastic analysis, optimiz…
further particulars: 168225 Job Description - Postdoctoral Research Associate in Deep Learning
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application closing date: 2023-12-04T12:00:00+00:00
application opening date: 2023-10-02T09:00:00+01:00

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168225 Job Description - Postdoctoral Research Associate in Deep Learning
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value: MATHEMATICAL INSTITUTE ANDREW WILES BUILDING Job Description and Selection Criteria Job title Postdoctoral Research Associate in Deep Learning 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: £38,205 - £44,263 per annum Hours Full time (37.5 hours per week) Contract type Fixed-term (36 months) Reporting to Professor Justin Sirignano Vacancy reference 168225 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. This post is subject to a 12-month probationary period. Additional information This post is funded by Engineering and Physical Sciences Research Council (EPSRC). (PLEASE NOTE: Applicants are responsible for contacting their referees and making sure that their letters are received by the closing date). Research topic Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks Principal Investigator / Prof. Justin Sirignano and Prof. Konstantinos Spiliopoulos supervisor Project team Prof. Justin Sirignano and Prof. Konstantinos Spiliopoulos https://math.bu.edu/people/kspiliop/ Project web site Funding partner Recent publications Research group https://www.maths.ox.ac.uk/people/justin.sirignano This research project is funded by a joint NSF-EPSRC grant to Boston University and the University of Oxford. The postdoctoral position at the University of Oxford is funded by EPSRC. 1. “Mean field analysis of neural networks: A law of large numbers.” J. Sirignano and K. Spiliopoulos. SIAM Journal on Applied Mathematics, 80 (2), 725-752, 2020. 2. “Mean field analysis of neural networks: A central limit theorem.” J. Sirignano and K. Spiliopoulos, Stochastic Processes and their Applications, 130 (3), 1820-1852, 2020. 3. “Mean Field Analysis of Deep Neural Networks.” …

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