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Network visualisation of high-dimensional search: developing data analytics for computational biology models, Computer Science – PhD (Funded)

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  • Full or part time
    Dr J Fieldsend
    Dr O Akman
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

About This PhD Project

Project Description

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences, is inviting applications for a fully-funded PhD studentship to commence in September 2019 or as soon as possible thereafter. For eligible students the studentship will cover UK/EU/International tuition fees plus an annual tax-free stipend of at least £15,009 for 3.5 years full-time, or pro rata for part-time study. The student would be based in Computer Science in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter.

Project Description:
The project will investigate and develop extensions to the local optima network framework for fitness landscape analysis of optimisation problems. Specifically, it will investigate methods for visualising continuous high-dimensional search, with real-world examples from the systems biology domain.
With the vast growth in scientific data, data visualisation methods have become ever more important. These are crucial to both bridge the gap between specialists and non-specialists (to aid the explanation of science and results), and also to investigate and probe the relationships within data (leading to new knowledge and discoveries).

One area where such approaches are important is when visualising the properties of a problem that affect optimiser performance. For instance, visualisation of the fitness landscapes relating designs to their corresponding quality, and broader differences between design regions, is useful -- but difficult -- as the data may naturally live in a high number of dimensions. In the last decade, local optima networks (LONs) have arisen as a useful and compact representation of the fitness landscape in combinatorial spaces. However, their extension to continuous spaces is less well explored and we have recently developed methods for extending the LON framework to multi-objective problems.

This College-funded PhD project, is closely aligned to the EPSRC-funded project, EP/N017846/1: The Parameter Optimisation Problem: Addressing a Key Challenge in Computational Systems Biology. The PhD project is concerned with the investigation and development of novel visualisation methods of the problem landscape associated with gene regulatory network models, with circadian clocks as a prototypical example. However, it is anticipated that the work will be applicable to a broader range of problems, across different disciplines (e.g. physics, engineering etc.)

The successful applicant will be embedded in a thriving research environment, which includes the Living Systems Institute: a £52.5 million investment into interdisciplinary approaches to understanding biological systems. The Computer Science and Mathematics departments at Exeter are dynamic and growing communities, with significant interactions, including a large and active body of postgraduate and postdoctoral researchers. The departments provide substantial support for postgraduate researchers, including the facility to attend appropriate Masters level modules alongside their PhD projects. In addition, the applicant will have access to a broad range of centrally provided training courses relevant to this project (e.g. courses on utilising Exeter’s new £3 million high performance computing facility).

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