Back to search results

PhD Studentship: Adaptive Predictive Simulation Modelling Using the Digital Twin Paradigm

Bournemouth University - Faculty of Science and Technology

Qualification Type: PhD
Location: Bournemouth
Funding for: UK Students, EU Students, International Students
Funding amount: £15,000
Hours: Full Time
Placed On: 25th March 2019
Closes: 2nd June 2019

Funding amount: £15,000 maintenance grant per annum

Lead Supervisor name: Dr Edward Apeh

Structural integrity management (SIM) involves the application of qualified standards, by competent people, using appropriate processes and procedures throughout the structure's life cycle, from design through to decommissioning, to ensure that through an ongoing process of risk management the structure's continued fitness-for-purpose (FFP) is maintained.

This project will investigate the application of data twinning to predict simulated models for large real-world structures. Using digital twin modelling, we aim to develop algorithms that update model predictors online. This will make it possible to investigate methods of adapting and changing simulation models online as the nature of the monitored structure changes. Adaptation helps to prolong the useful life of learned predictive/inference models which is an important part of making models more useful to non-expert users.

The developed methods will be tested and verified using the simulation models generated based on the current heuristics of using survey data collected from the machine systems of industrial partners.

The successful PhD candidate will be expected to investigate and develop methods for efficient and robust simulation model analytics and predictions that enable manufacturers to edit a virtual prototype throughout the production process and maintain the physical twin's continued fitness-for-purpose (FFP). The goal is to provide more accurate predicted structural models that reduce development and maintenance time and costs. Therefore, in this position, understanding of physical modelling and creation of these models is essential together with the analytics skills, i.e. combining mechanical/process engineering with data analytics. 

What does the funded studentship include?

Funded candidates will receive a maintenance grant of £15,000 per annum (unless otherwise specified), to cover their living expenses and have their fees waived for 48 months. In addition, research costs, including field work and conference attendance, will be met.

Funded Studentships are open to both UK/EU and International students unless otherwise specified.

Eligibility criteria

Candidates for funded PhD studentship must demonstrate outstanding qualities and be motivated to complete a PhD in 3 years.

Studentship candidates must demonstrate outstanding academic potential with a 1st class honours degree and/or a Master’s degree with distinction. An IELTS (Academic) score of 6.5 minimum (with a minimum 6 in each component) is essential for candidates for whom English is not their first language.

Additional Eligibility:

The candidate should hold MSc degree in a suitable field (Automation, Signal Processing, Information Sciences, Computational Intelligence, Computational Mechanics, etc.).

A theoretically oriented, problem-solving mind with experience of data analytics, and simulation and modelling of physical systems will be an added advantage on this PhD. Moreover, a good command in programming with R, Python, C/C++/Fortran is a necessity. Good team working skills will be expected.

Closing date: The first call for applications will close on 2 June 2019.

For further information on how to apply click the ‘Apply’ button below or email

We value your feedback on the quality of our adverts. If you have a comment to make about the overall quality of this advert, or its categorisation then please send us your feedback
Advert information

Type / Role:

Subject Area(s):


PhD tools
More PhDs from Bournemouth University

Show all PhDs for this organisation …

More PhDs like this
Join in and follow us

Browser Upgrade Recommended has been optimised for the latest browsers.

For the best user experience, we recommend viewing on one of the following:

Google Chrome Firefox Microsoft Edge