Funding

Self-funded PhD students only

Project code

COMP5401021

Department 

School of Computing

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.

The PhD will be based in the School of Computing and will be supervised by Dr Ioannis Kagalidis.

The work on this project could involve:

  • Working with real time or near real time image identification.
  • Working in developing innovative Artificial Intelligence solutions.
  • Evaluate the performance and behaviour of different approaches to semantic image identification.
Neural Networks have proven to be effective in image recognition tasks. The ability to extract semantic information from near real time images is still somewhat not fully explored and still remains relatively elusive.  Humans are able to recognise objects and their path through space in real time. Consider cars that wait at a red traffic light. Humans are able to pick one and follow it through to the next traffic light. For a standard AI system, any car is the same as the next.  Semantic identification will almost certainly become a necessity in applications coping with the need to understand concepts as abstract as “follow that yellow car” and “has the accident completely blocked the motorway?” which would provide a high level of interaction between humans and AI systems. The School of Computing in the University of Portsmouth has an established and ongoing collaboration with the PCC and private stakeholders to investigate the application of Neural Networks in better traffic managements and generation of traffic flow models. 

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).

2021/2022 fees (applicable for October 2021 and February 2022 start)

PhD and MPhil

Home/EU/CI full-time students: £4,500 p/a**

Home/EU/CI part-time students: £2,250 p/a**

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

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

PhD by Publication 

External candidates: £4,407*

Members of staff: £1,720 

All fees are subject to annual increase. 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

You'll need a good first 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.

  • Basic understanding of Artificial intelligence concepts.
  • Capable to carry out independent research.
  • Capable to carry out simulations and Artificial Neural Network code development and testing.
  • Capable to prepare research articles.

How to apply

We’d encourage you to contact Dr ioannis Kagalidis  (ioannis.kagalidis@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, please follow the 'Apply now' link on the Computing PhD subject area page and select the link for the relevant intake. 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.  Our ‘How to Apply’ page offers further guidance on the PhD application process. 

When applying please quote project code: COMP5401021

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