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UQDE - Uncertainty quantification and design of experiments

  • Full or part time
  • Application Deadline
    Sunday, December 01, 2019
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Uncertainty quantification for complex, expensive-to-evaluate models is a topic of high importance and intensive research activity in Statistics and Applied Mathematics, with numerous applications in Engineering, Climate Science, and Defence to name a few.

Key problems include the construction of cheaper surrogate models (emulation) using data from a computer experiment, optimization of models using sequentially refined surrogates, and inference for unknown parameters in the models using physical data (calibration). All of these tasks require the use of efficient experimental designs in order to produce accurate and precise results. This project will develop cutting-edge Bayesian statistical methodology to address the analysis and design questions in such studies, involving Gaussian Process models and Monte Carlo methods.

Candidates are normally expected to have an MMath (first class) or MSc (Distinction) degree in Mathematics/Statistics, or another closely related discipline with a strong statistical component. A successful applicant will have the ability to work independently on challenging problems and have, or be willing and able to develop, good scientific programming skills and/or strong statistical computing skills (R/Julia/Matlab/C++).

Provisional timeline for applications:
- Deadline for response 1 December 2019
- Interviews December/January
- Academic offer by 20 January 2020
- Funding decision by 5 February 2020
- Project start September 2020

Supervisor: Dr Tim Waite ()

Funding Notes

Full funding available for UK and EU students, competitively awarded. Partial funding is also available for international students, again awarded competitively.

How good is research at The University of Manchester in Mathematical Sciences?

FTE Category A staff submitted: 54.40

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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