Computer Science: Fully Funded Swansea University and QinetiQ PhD: Explainable artificial intelligence for automated knowledge discovery and scientific trend prediction (RS565)

Funding providers: Swansea University's Faculty of Science and Engineering and QinetiQ 

Subject areas: Artificial Intelligence, Computer Science

Project start date: 

• 1 July 2024 (Enrolment open from mid-June)

Supervisors:

• Professor Jiaxiang Zhang (Jiaxiang.zhang@swansea.ac.uk)
• Dr Nicholas Micallef

Aligned programme of study: PhD in Computer Science

Mode of study: Full-time

Project description: 

The number of scientific papers published is increasing rapidly every year. The overwhelming volume of the literature makes it challenging to keep up with the latest research and developments. Working closely with the project partner QinetiQ, this project will combine state-of-the-art machine learning methods, natural language models and image recognition to develop a human-in-the-loop system for systematic reviews and predictive analytics. We envisage that the system will be able to collate and analyse a significant amount of research publications. Furthermore, we expect the system to quantify and extrapolate research trends that may have significant impacts on technology development. Our key objectives are:   

1. Optimise an automated systematic review system that integrates texts, figures and tables from scientific publications and other credible sources;
2. Develop and validate deep neural network models that predict scientific trends and their impacts;
3. Deliver an integrated interface that renders model outputs with explainability, transparency and efficiency.

The project partner, QinetiQ, is a multinational FTSE 250 company with a global workforce exceeding 6000 employees. It offers world-class expertise in advice, services, and innovative technology-based products in the manufacturing, security, defence, finance, energy, and telecom sectors.  This collaborative research initiative signifies QinetiQ's commitment to informed investment in AI and advanced computing. The overarching goal is to pioneer leading-edge solutions, particularly in the domains of application-specific analytics and data science. Through this joint effort, QinetiQ aims to push the boundaries of innovation, ensuring they remain at the forefront of technological advancements in these emerging and critical areas. 

Candidates must hold an undergraduate degree at 2.1 level in Computer Science, Mathematics or a closely related discipline, or an appropriate master’s degree with a minimum overall grade at ‘Merit’ (or Non-UK equivalent as defined by Swansea University). If you are eligible to apply for the scholarship (i.e. a student who is eligible to pay the UK rate of tuition fees) but do not hold a UK degree, you can check our comparison entry requirements (see country specific qualifications). Please note that you may need to provide evidence of your English Language proficiency. 

Due to funding restrictions, this scholarship is open to applicants eligible to pay tuition fees at the UK rate only, as defined by UKCISA regulations. 

If you have any questions regarding your academic or fee eligibility based on the above, please email pgrscholarships@swansea.ac.uk with the web-link to the scholarship(s) you are interested in. 

This scholarship covers the full cost of UK tuition fees and an annual stipend at UKRI rate (currently £18,622 for 2023/24).

Additional research expenses will also be available.

To apply, please complete your application online with the following information:

1. Course choice – please select Computer Science / PhD / Full-time / 3 Years / July

   In the event you have already applied for the above programme previously, the application system may issue a warning notice and prevent application, in this event, please email pgrscholarships@swansea.ac.uk where staff will be happy to assist you in submitting your application.

2. Start year – please select 2024
3. Funding (page 8) –

• ‘Are you funding your studies yourself?’ – please select No
• ‘Name of Individual or organisation providing funds for study’ – please enter ‘RS565 - Explainable Artificial Intelligence’

*It is the responsibility of the applicant to list the above information accurately when applying, please note that applications received without the above information listed will not be considered for the scholarship award.

One application is required per individual Swansea University led research scholarship award; applications cannot be considered listing multiple Swansea University led research scholarship awards.

We encourage you to complete the following to support our commitment to providing an environment free of discrimination and celebrating diversity at Swansea University: 

• Equality, Diversity and Inclusion (EDI) Monitoring Form (online form) 

As part of your online application, you MUST upload the following documents (please do not send these via email). We strongly advise you to provide the listed supporting documents by the advertised closing date, where possible:

• CV
• Degree certificates and transcripts (if you are currently studying for a degree, screenshots of your grades to date are sufficient)
• A cover letter including a ‘Supplementary Personal Statement’ to explain why the position particularly matches your skills and experience and how you choose to develop the project.
• Two references (academic or previous employer) on headed paper or using the Swansea University reference form. Please note that we are not able to accept references received citing private email accounts, e.g. Hotmail. Referees should cite their employment email address for verification of reference.
• Evidence of meeting English Language requirement (if applicable).
• Copy of UK resident visa (if applicable)
• Confirmation of EDI form submission (optional) 

Informal enquiries are welcome, please contact Professor Jiaxiang Zhang (Jiaxiang.zhang@swansea.ac.uk).

*External Partner Application Data Sharing – Please note that as part of the scholarship application selection process, application data sharing may occur with external partners outside of the University, when joint/co- funding of a scholarship project is applicable.