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  High-dimensional computations with applications to uncertainty quantification for multiphysics engineering systems

   Department of Mathematics & Statistics

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  Dr Yoshihito Kazashi, Dr Sifeng Bi, Dr Marco De Angelis  Applications accepted all year round  Funded PhD Project (UK Students Only)

About the Project

The Strathclyde Centre for Doctoral Training (SCDT) in "Data-driven uncertainty-aware multiphysics simulations" (StrathDRUMS) is a new, multi-disciplinary centre of the University of Strathclyde, which will carry out cutting-edge research in data-driven modelling and uncertainty quantification for multiphysics engineering systems. StrathDRUMS will train the next generation of specialists to apply non-deterministic model updating, digital twin techniques, and advanced uncertainty treatments to real-world challenges in civil and aerospace engineering. In our research we aim to study complex systems such as aeroplanes, spacecrafts, buildings and bridges using rigorous mathematical concepts, formulations and computational methods.

We are pleased to announce an available funded 3.5 year PhD studentship project within the centre on “High-dimensional computations with applications to uncertainty quantification for multiphysics engineering systems”, supervised by Dr Yoshihito Kazashi in the Department of Mathematics and Statistics and co-supervised by Dr Sifeng Bi (Mechanical & Aerospace Engineering), Dr Marco de Angelis (Civil & Environmental Engineering). In engineering applications, various types of uncertainty are modelled with a large number of parameters. However, working with such models yields high-dimensional problems, such as high-dimensional integration to compute expectations and high-dimensional approximation to construct surrogate models, that are computationally very challenging. The successful candidate will develop and analyse numerical techniques in computational uncertainty quantification. They will focus specifically on cutting-edge numerical analysis of high-dimensional integrals (that outperform Monte Carlo methods), function approximation, probability density function estimation as well as optimal solvers of partial differential equations that take uncertainty into account.

Environment and Training

Although the student will be based in the Department of Mathematics and Statistics at the University of Strathclyde, they will be benefit from supervision by the wider SCDT team. According to the student interest and background, the research will be aligned and supported by one of StrathDRUMS partners, which include National Manufacturing Institute Scotland (NMIS), National Physical Laboratory (NPL), and UK Atomic Energy Authority (UKAEA). The student will thus be integrated within a vibrant and active multi-disciplinary research community with in-house training opportunities available across multiple faculties. In addition to undertaking cutting-edge research, the student will be registered for the Postgraduate Certificate in Researcher Development (PGCert), which is a supplementary qualification to develop core skills, networks, and career prospects.


The successful candidate will be expected to conduct high-quality research in the areas of computational uncertainty quantification, participate in relevant training activities and events provided by StrathDRUMS, disseminate research findings through publications and presentations, contribute to the wider research community through engagement and collaboration with other researchers.


We encourage applications from UK-based students for this position. However, we also welcome strong international students to apply, provided they are able to cover the difference between the home and international tuition fees on their own.


Applicants should have, or be expecting to obtain soon, a first class or good 2.1 honours degree (or equivalent) in mathematics or in a closely related discipline with a high mathematical content. Excellent written and verbal communication skills, analytical and problem-solving skills, ability to work independently and as part of a team are essential. Programming skills and some knowledge of analytical and numerical methods in uncertainty quantification are desirable.


To apply, candidates should send their CV, transcript, and cover letter to [Email Address Removed]. Applications will be accepted until the position is filled.

Computer Science (8) Engineering (12) Mathematics (25)

Funding Notes

The studentship should start on 1 October 2024. The studentship will fund the annual Home tuition fees and a tax-free stipend for 3.5 years. The stipend rates are announced annually by UKRI. To give an idea, for the 2023/24 academic year, the annual UKRI stipend is £18,622.

Where will I study?

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