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  Investigating the role of verified methods for enhancing trust in digital twins


   Department of Civil & Environmental Engineering

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  Dr Marco De Angelis, Dr Sifeng Bi, Dr Yoshihito Kazashi, Dr Michele Ruggeri  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 “Investigating the role of verified methods for enhancing trust in digital twins”, supervised by Dr Marco de Angelis (Civil & Environmental Engineering) and co-supervised by Dr Sifeng Bi (Mechanical & Aerospace Engineering), and by Dr Yoshihito Kazashi and Dr Michele Ruggeri (Mathematics & Statistics). In engineering, trustworthy simulations are at the very centre of efficient manufacturing and monitoring. The explicit account of the uncertainty within simulation is still a challenge, because of the prohibitive cost of the Monte Carlo method. In this project, we investigate the role of verified methods to efficiently propagate the uncertainty on engineering problems such as structural vibrations and deflections. The successful candidate will review and apply numerical methods in computational uncertainty quantification. The focus will be specifically on verified methods for the solution of ordinary and partial differential equations with uncertain initial and boundary conditions. Verified methods will also be applied in conjunction with validation methods for the rigorous integration of empirical data within the simulation environment.

Environment and Training

The student will be based in the Department of Civil and Environmental Engineering at the University of Strathclyde, while benefitting from shared supervision by the wider SCDT team. The student interests and background will be aligned to 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 team 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.

Role

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.

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.

Qualifications

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.

Application

To apply, candidates should apply at here . Yo must upload a CV, transcript, and cover letter for your application to be considered. 


Engineering (12) Mathematics (25)

Funding Notes

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 2022/23 academic year, the current annual UKRI stipend is £17,668. International students may be considered if they are able to fund the fee differential.

Where will I study?

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