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Funding and scholarships for students

Award details

Accelerating Uncertainty Quantification with Deep Learning. EMPS College Home fees Studentship, PhD in Engineering Ref: 4313

About the award

Supervisors

Lead Supervisor: 

Prof Tim Dodwell - College of Engineering, Mathematics and Physical Sciences, University of Exeter, Streatham Campus

Co-supervisor: 

Mr Mikkel Bue Lykkegaard - College of Engineering, Mathematics and Physical Sciences, University of Exeter, Streatham Campus

Prof Richard Everson - College of Engineering, Mathematics and Physical Sciences, University of Exeter, Streatham Campus

 

Location:

Department of Engineering, Streatham Campus, Exeter, Devon, University of Exeter.

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.

Project Description:

Uncertainty Quantification (UQ) for engineering models is a rapidly growing field with numerous exciting applications. However, the current best-performing algorithms for quantifying the uncertainty through Markov Chain Monte Carlo (MCMC) rely on computing a gradient that is typically not readily available for complex engineering models. This project is concerned with investigating the potential of emerging methods from Machine Learning and Artificial Intelligence to construct efficient MCMC proposals that do not require this gradient.

The MCMC methodology at large targets a broad class of problems known Bayesian inverse problems which are ubiquitous to many areas of engineering. There are various apparent ways that the MCMC methodology could be improved by exploiting other established methods borrowed from e.g. Reinforcement Learning and Artificial Neural Networks, but it is currently not clear exactly how to do this, and how such an algorithm will perform in real-world engineering problems. We can provide various relevant benchmark problems for testing, but you are free to explore other applications once you have familiarised yourself with the topic at large. Aside from developing and testing viable methods, this project also involves writing reusable computer code implementing these methods. There are opportunities to contribute to existing open-source code, but no requirement to do so.

Since this is a broad topic with many promising avenues of research, we strongly encourage independent thought and creativity. There are numerous open questions and research opportunities within the current state-of-the-art algorithms, in particular within the subtopics of Delayed Acceptance (DA) and Multi-level MCMC (MLMCMC), around which the MCMC research in our group mainly revolves. While we encourage you to nestle your research within this frame of reference, you will have the freedom to explore other feasible approaches.

The successful applicant will have a high level of numeracy and be proficient with computer programming since this project involves converting complex mathematical concepts to reusable computer code. Your programming language of choice is not essential, but knowledge of Python or C++ would be advantageous.

Entry requirements

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, preferably in probability and statistics. Experience of coding in Python and the use of deep learning libraries, such as PyTorch or TensorFlow, is desirable.

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”.

• CV
• 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 pgrenquiries@exeter.ac.uk 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 10th January 2022. Interviews will be held online on the week commencing 24th January 2022.

If you have any general enquiries about the application process please email pgrenquiries@exeter.ac.uk

Project-specific queries should be directed to the main supervisor at T.Dodwell@exeter.ac.uk

Summary

Application deadline:10th 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 Office pgrenquiries@exeter.ac.uk