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  Computationally efficient modelling & simulation and applications


   Department of Automatic Control and Systems Engineering

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The advent of easily accessible high performance computers or computer clusters and numerical techniques such as finite element methods (FEM) facilitates the highly accurate modelling and simulation of complex systems in diverse science and engineering disciplines including civil engineering, biomechanics and life science. However, the exciting developments in obtaining highly accurate models are leading to very challenging computational questions related to the computational efficiency and effectiveness. To address these challenges, many computationally efficient modelling and simulation techniques have been and are still being developed such as reduced-order modelling and surrogate modelling in recent years. We have established a range of collaborations to facilitate the application studies including, for example, reduced-order modelling of muscle forces and bone remodelling process, and surrogate modelling for FE simulation of bone mechanics. This project aims to tackle the computational challenges arising in complex system modelling and simulation, by developing new computationally efficient modelling and simulation approaches, able to answer urgent questions in complex system problems.

Computer Science (8) Mathematics (25)

Funding Notes

This is a self-funded research project.
We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable institution.
Prospective candidates for this project should have a background in Engineering, Applied Mathematics, or related disciplines.
Full details of how to apply can be found at the following link:
View Website
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: View Website

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