Funding

Self-funded

Project code

CMP10001025

Department

School of Computing

Start dates

October, February and April

Application deadline

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 Mo Adda, Dr Alex Gegov and Dr Gelayol Golcarenarenji.

The work on this project will:

  • Address the need to help law enforcement agencies and authorities to investigate criminal activities committed by smart machines such as Vehicles, drones, Robots and the like.  
  • Apply machine learning algorithms and AI techniques around image identification and text processing and searching to classify and filter the evidence collected by the SpiderNet or the cloud. 
  • Develop a prototype system that could become a commercial product for the cloud service providers.

The advancement of the Internet of Things (IoT), robots, drones, and vehicles signifies ongoing progress, accompanied by increasing complexities and challenges in forensic investigations. Globally, investigators face obstacles when extracting evidence from these vast landscapes, which include diverse devices, networks, and cloud environments. Of particular concern is the process of evidence collection and classification, especially concerning fingerprints and facial recognition within the realm of vehicle forensics. Mitigating these challenges, along with addressing evidence mobility, presents additional complexities to law enforcement agencies.

 

This PhD project focuses on classifying, filtering, and producing accurate collected evidence to help investigators identify and prosecute criminals more quickly and efficiently. Additionally, it aims to save space in public or private data centers that handle massive amounts of collected evidence. The project will explore the SpiderNet infrastructure based on the cloud, identifying optimal locations such as fog and edge servers to minimize latencies and response times when collecting and analyzing evidence. This project will demonstrate how this architecture facilitates the identification of devices, secures the integrity of evidence both at its source and during transit, and provides comprehensive support for law enforcement.

 

Fees and funding

Visit the research subject area page for fees and funding information for this project.

Funding availability: Self-funded PhD students only. 

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

 

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 (minimum upper second class or equivalent, depending on your chosen course) or a master’s degree in computer science or a related area. 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.

You must have skills in machine learning, computer vision, and image identifications.

Desirable skills in cloud computing, networks, and cyber security.

 

How to apply

We’d encourage you to contact Dr Mo Adda  (mo.adda@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: CMP10001025