PhD Studentship: Deep Optimisation

3 days left

United Kingdom
Jan 29, 2016
Jan 27, 2017
Organization Type
University and College
Full Time

PhD Studentship: Deep optimisation

Engineering & the Environment

Location:  Highfield Campus
Closing Date:   Friday 27 January 2017
Reference:  684916F2

Project Reference: NGCM-0078

This project will develop the implementation and application of a new optimisation technique. “Deep optimisation” combines deep learning techniques in neural networks with distributed optimisation methods to create a dynamically re-scalable optimisation process. This project will develop this technique to better-understand its capabilities and limitations and develop GPU implementations. The protein structure prediction problem will be used as the main test application.

In engineering optimisation and design, the idea of breaking a problem into sub-problems, solving the sub-problems and then assembling sub-solutions together, is familiar and intuitive. However, such problem decomposition is notoriously difficult to automate because knowledge of how to decompose the problem – how to ‘divide it at its natural joints’ – is required. Meanwhile, deep learning techniques in neural networks, inspired by recent high-profile successes, have received a lot of attention. These are neural network methods that build hierarchical representations of a problem domain in a distributed but hierarchical manner. A key innovation is to achieve this in a layer-by-layer bottom-up manner. Deep Optimisation is a new technique that aims to achieve automated problem decomposition by bringing deep learning and distributed optimisation processes together.

The successful candidate will develop and test algorithms based on this new approach using GPU programming on large scale distributed compute clusters. These will be applied to the protein structure prediction problem and compared with existing state-of-the-art methods. The successful candidate must be expert in relevant computational methods. Experience of bioinformatics problems/techniques is desirable.

If you wish to discuss any details of the project informally, please contact Richard Watson, AIC research group, Email:

This project is run through participation in the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling ( For details of our 4 Year PhD programme, please see

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