EPSRC Industrial CASE PhD Studentship (3 years) - Cascading RBFs: A New Paradigm for Layered Machine Learning

Aston University

Applications are invited for a fully supported plus top-up, three-year eCASE Postgraduate studentship leading to a PhD. The studentship is funded by the Engineering and Physical Sciences Research Council (EPSRC) and Thales Research & Technology, based at Reading. The student will be working within the Systems Analytics Research Institute across the Mathematics and Computer Science groups at Aston University. The successful applicant will join a small team developing fundamental and applicable advanced machine learning and visual analytics.

The position is available to start in April 2018 (or later by agreement) and is 3 years in duration

Financial Support

This studentship includes a fee bursary to cover the home/EU fees rate, plus a maintenance allowance of £14,553 in 2017/18 (subject to eligibility) plus a £3,000 per year award from the collaborating company. This application is only available to Home/EU students.

Background of the Project

Current state-of-the-art approaches to deep learning for big data analysis suffer from the problem of “adversarial examples” --- input patterns of one class which are imperceptible perturbations of another pattern and yet are assigned to completely different classes by the deep learning architecture. The project will investigate a new type of layered machine learning architecture invented at Aston University known as a cascading radial basis function network, which does not suffer from the same problems as current deep learning approaches yet still retains impressive learning capability. The architecture can be applied to time series, images, tensor data, and non vectorial data problems and so has huge potential for advancing state of the art in several application domains.

The student will be researching methods to improve the algorithms to scale to millions of patterns, implementing the architecture on various real world problem domains, and developing new understanding on the capabilities of this alternative machine learning approach.

Person Specification

The successful applicant should in the first instance be highly motivated and curious. They should also generally have a first class or upper second class honours degree or equivalent qualification in theoretical physics, applied mathematics, computational mathematics, electronic or mechanical  engineering systems, or similar. Prior knowledge of machine learning is not essential, but commitment to learn and understand is. Applicants should fulfil the eligibility criteria for EPSRC funding through UK nationality and/or residency status (see www.epsrc.ac.uk).

For informal enquiries about this project and other opportunities within the Systems Analytics Research Institute contact Dr Yulan He (y.he9@aston.ac.uk) or Professor David Lowe (d.lowe@aston.ac.uk).

If you require further information about the application process please contact the Postgraduate Admissions team at seasres@aston.ac.uk

The online application form, reference forms and details of entry requirements, including English language are available at www1.aston.ac.uk/eas/research/prospective-research-students/how-to-apply

Applications should also be accompanied by a brief review  (2 pages) of relevant research literature appropriate for this project area.

Details of how to write your project proposal are also included in the How to Apply section

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