Department of Computer Science, University of Exeter, Streatham Campus, Exeter, Devon.
The University of Exeter’s College of Engineering, Mathematics and Physical Sciences is inviting applications for a fully-funded PhD studentship to commence as soon as possible. The studentship will cover Home tuition fees plus an annual tax-free stipend of at least £15,609 for 3.5 years full-time, or pro rata for part-time study.
This College studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.
It is often convenient to represent information in a specific domain using graphs: it is in fact possible to interrogate experts and ask them about the main entities in their fields of interest (these will constitute the nodes) and their relationships (these will be the edges). In biochemistry, for example, the entities of interest can be atoms and their relationships chemical bonds or the entities of interest could be large biomolecules such as proteins and their relationships various types of interactions.
Many interesting and impactful problems can be formulated as optimization problems over graphs, for example the synthesis of a novel drug can be seen as an optimization problem where one wants to find the molecular graph with the highest biological activity. Another interesting problem is that of the identification of the gene regulatory network that best explains proteins expression data; this knowledge would then help understand the difference between healthy and pathological conditions and therefore guide scientists in treating the underlying diseases.
Being able to build computational models that can learn to perform these types of optimisations can allow us to tackle important challenges as one can improve existing entities according to multiple desired objectives and obtain, for example, drugs that are highly effective, but at the same time, minimally toxic.
In this project you are going to explore different ways to design optimization algorithms over graphs, that adapt to a given problem using Machine Learning techniques.
The two main computational approaches to process graphs are ""graph neural networks"" and ""graph kernels"". They each have advantages and disadvantages: graph neural networks are very flexible but require large amount of data and their predictions are hard to explain and understand; graph kernels are efficient but need to be carefully designed for each specific problem.
When tackling biological problems, however, we often require a clear and understandable solution obtained using the smallest number of experiments since these are expensive and time consuming. Ultimately, we envision that the outcome of this project will be the integration of connectionist and kernel approaches to obtain robust and efficient optimisation procedures that can help scientist in the refinement and improvement of complex biological entities.
In order to take this project, it is essential to have excellent mathematics and machine learning background knowledge as well as good programming skills.
This studentship is open to UK and Irish nationals, who if successful in their application will receive a full studentship including payment of university tuition fees at the home fees rate.
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 an appropriate area of science or technology.
First or Upper Second Class UK Honours degree in computer science, mathematics, physics.
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 http://www.exeter.ac.uk/postgraduate/apply/english/).
How to Apply
In order to formally apply for the PhD Project you will need to go to the following web page.
The closing date for applications is 31st May 2022.
If you have any general enquiries about the application process please email [Email Address Removed] or phone: 0300 555 60 60 (UK callers) or +44 (0) 1392 723044 (EU/International callers). Project-specific queries should be directed to the main supervisor.