Self-funded PhD students only

Project code



School of Civil Engineering and Surveying

Start dates

October and February

Closing date

Applications accepted all year round

Applications are invited for a self-funded 3-year full-time or 6-year part-time PhD project, to commence in October or February.

The PhD will be based in the Faculty of Technology, and will be supervised by Dr Jiye Chen

The work on this project will:

  • Collect/conduct camera images with multiscale damage, including macro and micro damage to build up the capability of artificially intelligent camera imaging algorithm (AIMCIA) to graphically recognise visible and invisible damage.
  • Develop the Image Benchmarks of Multiscale Damage (IBMD), including various common crack damage and corrosion damages into the image database, to underpin the AIMCIA.
  • Develop the proposed AIMCIA in terms of Convolutional Neural Networks with Multitask Learning technology for graphically recognising various types of multiscale damage.
  • Verify/adjust the AIMCIA and IBMD using selected images with common cracks and corrosion damages, especially damage mixed with dust particles, and invisible damage at the micro scale.
  • Validate the proposed AIMCIA using objects such as a marina seawater tank, a wing box or a blade joint in airplanes, selected by leading industrial partners.

This PhD project aims to develop a novel artificially intelligent camera imaging algorithm (AIMCIA) for the live monitoring of multiscale damage in engineering objects. A hybrid methodology with collection and conduction of camera images, image database creation and image algorithm development and analysis will be applied in this investigation. The challenging work is establishing the IBMD as well as developing the AIMCIA.

Image photogrammetry can be used to select and edit collected images and cloud data technology can be used to catalogue images and store them into the proposed IBMD. A deep learning technology based on the concept of Convolutional Neural Networks with Multitask Learning will be used to develop the proposed AIMCIA. It will be justified using selected images from the IBMD and validated by the objects selected by industrial partners.

Fees and funding

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

2020/2021 entry (for October 2020 and February 2021 entries)

Home/EU/CI full-time students: £4,407 p/a

Home/EU/CI part-time students: £2,204 p/a

International full-time students: £16,400 p/a

International part-time students: £8,200 p/a

PhD by Publication

External candidates £4,407 p/a

Members of staff £1,680 p/a*

2021/2022 entry (for October 2021 and February 2022 entries)

PhD and MPhil

Home/EU/CI full-time students: £4,407 p/a*

Home/EU/CI part-time students: £2,204 p/a* 

International full-time students: £17,600 p/a

International part-time students: £8,800 p/a

All fees are subject to annual increase.

PhD by Publication

External Candidates £4,407 p/a*

Members of Staff £1,720 p/a*

If you are an EU student starting a programme in 2021/22 please visit this page.

*This is the 2020/21 UK Research and Innovation (UKRI) maximum studentship fee; this fee will increase to the 2021/22 UKRI maximum studentship fee when UKRI announces this rate in Spring 2021. 

Bench fees

Some PhD projects may include additional fees – known as bench fees – for equipment and other consumables, and these will be added to your standard tuition fee. Speak to the supervisory team during your interview about any additional fees you may have to pay. Please note, bench fees are not eligible for discounts and are non-refundable.

Entry requirements

Entry requirements

You'll need an upper second class honours degree from an internationally recognised university or a Master’s degree in an appropriate subject. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Ideally, you should have a first degree in an appropriate subject, e.g. Computer Science or Computing. A Postgraduate qualification related to Computer Science, Computer image process or Computer Graphical recognition would be welcomed.

How to apply

We’d encourage you to contact Dr Jiye Chen ( to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. An extended statement as to how you might address the proposal would be welcomed.

Our ‘How to Apply’ page offers further guidance on the PhD application process.

If you want to be considered for this self-funded PhD opportunity you must quote project code SCES4560220 when applying.

October start

Apply now

February start

Apply now