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PhD Studentship - Protein ghost recognition using deep learning

Sustainable Electronic Technologies

Location:  Highfield Campus
Closing Date:  Monday 31 August 2020
Reference:  1201919FP

Supervisor:                 Prof Andrew Brown

Project description

POETS (Partially Ordered Event Triggered Systems) technology is based on the idea of an extremely large number (millions) of small processing cores, embedded in a fast, bespoke, hardware, parallel communications infrastructure – the core mesh. Inter-core communication is asynchronous, and effected by small, fixed size, hardware data packets (a few bytes) – messages. For an important set of industrial problems, POETS architectures are capable of delivering orders of magnitude speed increases at significantly lower power levels.

The technology lends itself elegantly to a variety of neural network-type applications; one such area is that of ghost pattern recognition.

Cell biology - at the molecular level - underpins much research into biological processes and disease pathways. Understanding how pathogens interact with proteins embedded in a cell surface is critical to designing any kind of defence, and to do this we have to know the structure of all the proteins involved. These will typically be large, chain-like organic molecules. The traditional method of establishing the structure of any large, complex molecule is to purify a sample of it, crystallise it, and illuminate it with high-intensity beams of X-rays or neutrons. The resulting diffraction pattern provides valuable insights to the structure of the molecule under study (this was how the spiral structure of DNA was originally discovered). It has long been known that there exists a class of molecules that are permanently disordered - they simply have no crystalline form. The timeliness of this project lies in the fact that researchers are now beginning to realise that this class of disordered molecules is far larger than was previously thought, and a large number of these enigmatic structures have biological relevance. This obviously provides a driver for the elucidation of their structure, but traditionally obtained diffraction patterns are not sufficiently clear for automatic extraction of structural information

This research project will focus on the construction of a neural network - underpinned by POETS technology - that is capable of recognising physical structure from badly-defined diffraction patterns that are incapable of resolution by traditional methods.

POETS Website 

If you wish to discuss any details of the project informally, please contact Professor Andrew Brown, Sustainable Electronic Technologies (SET)Research Group, Email: adb@ecs.soton.ac.uk Tel: +44 (0) 2380 593374 OR Dr David Thomas d.thomas1@imperial.ac.uk Imperial College London +44 (0)2075 946303.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 31 August 2020 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: This 3.5 year studentship covers UK tuition fees and provides an annual tax-free stipend at the standard EPSRC rate, which is £15,009 for 2019/20.

How To Apply

Applications should be made online here selecting “PhD Electronic and Electrical Engineering (Full time)” as the programme. Please enter Andrew Brown under the proposed supervisor.

Applications should include

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online: https://www.southampton.ac.uk/courses/how-to-apply/postgraduate-applications.page

For further information please contact: feps-pgr-apply@soton.ac.uk 


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