Machine Learning to Quantify Uncertainty. EMPS College Home fees Studentship, PhD in Computer Science Ref: 4311
About the award
Lead Supervisor: Name, Campus, University of Exeter
Prof Tim Dodwell - College of Engineering, Mathematics and Physical Sciences, University of Exeter.
Co-supervisor: Name, Campus, University of Exeter
Dr George De Ath - College of Engineering, Mathematics and Physical Sciences, University of Exeter.
Prof Richard Everson - College of Engineering, Mathematics and Physical Sciences, University of Exeter.
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 in January 2022 or as soon as possible thereafter. 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.
Computational models of physical and biological processes (e.g., the climate system or the heart), of engineered systems (e.g., bridges, buildings, aircraft engines), and of social systems (e.g., the transmission and spread of disease or memes on a social network) are ubiquitous and widely used in decision making. However, these models often lack built-in estimates of the uncertainty in their output. Similarly, many machine learning models (such as random forests or support vector machines) are not based on probabilistic models and also do not provide uncertainty estimates.
This project will investigate and develop new machine learning methods that can be used to attach uncertainty quantification to any model that processes input data to yield outputs. Of particular interest are ensemble methods, such as random forests, in which an ensemble of weak predictors are combined to form a strong predictor, but we will aim to develop general broadly applicable tools.
Bayesian optimisation of expensive-to-evaluate functions relies on reliable uncertainty quantification and an additional focus for this project will be the incorporation of the new tools into Bayesian optimisers.
Expected outputs of the project are publications in top machine learning and optimisation conferences and journals, together with an open source suite of tools embodying the new methods.
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.
Candidates should have a strong mathematical background, specifically in probability and statistics. Your will also have programming experience and an interest in producing high quality software tools.
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 the application process you will be asked to upload several documents. Please note our preferred format is PDF, each file named with your surname and the name of the document, eg. “Smith – CV.pdf”, “Smith – Cover Letter.pdf”, “Smith – Transcript.pdf”.
• Letter of application (outlining your academic interests, prior project work 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 firstname.lastname@example.org 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. Please see the entry requirements information above.
The closing date for applications is midnight on 24th January 2022. Interviews will be held online on the week commencing 7th February 2022.
If you have any general enquiries about the application process please email email@example.com.
Project-specific queries should be directed to the main supervisor at T.Dodwell@exeter.ac.uk
|Application deadline:||24th January 2022|
|Value:||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.|
|Duration of award:||per year|
|Contact: PGR Admissions Officefirstname.lastname@example.org|