Bayesian Optimisation for Graphs, Computer Science, Mathematics – PhD (Funded) Ref: 3183

About the award


Prof Richard Everson, University of Exeter
Dr Fabrizio Costa, University of Exeter
Dr Alma Rahat , University of Exeter

The University of Exeter’s College of Engineering, Mathematics and Physical Sciences is inviting applications for an EPSRC NPIF PhD studentship to commence in September 2018.  This project is one of two projects that are in competition for funding from the EPSRC NPIF scheme. For eligible students the studentship will cover tuition fees plus an annual tax-free stipend of at least £14,777 for 4 years full-time, or pro rata for part-time study.  The student would be based in the Innovation Centre or Harrison Building in the College of Engineering, Mathematics and Physical Sciences at the Streatham Campus in Exeter.

Many real world engineering optimization problems consist of computationally expensive objective functions. In these settings traditional optimization methods that require thousands of function evaluations are impractical. Bayesian optimization has been shown to be an efficient approach to solve such problems when the domain is  low-dimensional and continuous. In many engineering problems, however, it is often natural to adopt a relational representation where instances are encoded as large graphs with relationships (edges) between entities (nodes) having discrete and continuous attributes. Typical examples are the design of water and communication networks. Currently, expensive optimization in relational domains is not well developed. We therefore propose to study how to exploit recent advances on graph kernels and machine learning based graph synthesis to enable Gaussian processes – the main component of Bayesian optimization – to operate directly in graph domains. The project aim is to develop a system for expensive optimization in relational domains using data driven adaptive procedures to learn both the problem constraints and computationally inexpensive versions of the objective functions. 

The studentship will cover a stipend at the minimum Research Council rate £14,777 per annum (for 2018/19), research costs and tuition fees.  This project is advertised under the EPSRC NPIF training grant under which a limited number of studentships can be allocated with open eligibility. This means students from outside of the UK are eligible to apply for this studentship.

Entry requirements

Applicants for this studentship must have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK, in Computer Science, Applied Mathematics, Physics, Electronic or Mechanical Engineering. Applicants with some training in dynamical systems and neural networks will be prioritized.
If English is not your first language you will need to have achieved at least 6.0 in IELTS and no less than 6.0 in any section by the start of the project.  Alternative tests may be acceptable (see

How to apply

In the application process you will be asked to upload several documents.
• CV
• Letter of application (outlining your academic interests, prior research experience and reasons for wishing to undertake the project).
• Transcript(s) giving full details of subjects studied and grades/marks obtained (this should be an interim transcript if you are still studying)
• Two references from referees familiar with your academic work. If your referees prefer, they can email the reference direct to quoting the studentship reference number.
• If you are not a national of a majority English-speaking country you will need to submit evidence of your proficiency in English

The closing date for applications is midnight on 12 July 2018.  Interviews will be held on the University of Exeter Streatham Campus or via Skype in late July 2018.
If you have any general enquiries about the application process please email or phone +44 (0)1392 722730.  Project-specific queries should be directed to the main supervisor.


Application deadline:12th July 2018
Number of awards:1
Duration of award:per year
Contact: Postgraduate Research Office