Computer Science: Fully Funded PhD Scholarship: Elevating Comparative Judgement Using a Large-Scale Human-In-The-Loop Bayesian Active Learning Approach (ECSTATIC) (RS590)

  • DeadlineDeadline: 1 May 2024
  • WalesWales


Closing date: 1 May 2024

Fully Funded Swansea and No More Marking Ltd PhD Scholarship: Elevating Comparative Judgement Using a Large-Scale Human-In-The-Loop Bayesian Active Learning Approach (ECSTATIC) 

Funding providers: Swansea University's Faculty of Science and Engineering and No More Marking Ltd

Subject areas: Human-Centred Artificial Intelligence, Optimisation, and Decision-Making

Project start date: 

  • 1 October 2024 (Enrolment open from mid-September)

Project supervisors: 

Aligned programme of study: PhD in Computer Science

Mode of study: Full-time

Project description: 

The Comparative Judgment (CJ) method, which has gained traction in UK schools over the past decade, involves assessors choosing the superior submission from a pair, rather than assigning a score to each one. This approach is less taxing for assessors and maintains accuracy for a small number of submissions. Recently, we developed a Bayesian active learning approach for CJ (BCJ;, to solve a crucial problem of interaction-efficient pair selection while producing reliable estimations of ranks and predictive uncertainty.  

In this related project, for the first time, we will aim to scale BCJ to handle thousands of items (as opposed to tens of them), enabling ranking and scoring across schools and assignments. We will propose new methods to dynamically incorporate new items for ranking in BCJ in an interaction-efficient manner, and devise avenues for providing individual learners insight into their progress over time compared to their peers. We will evaluate these methods to establish their efficacy in helping assessors make informed decisions under uncertainty arising from the practical paucity of data and interactions, as well as better informing learners. These methods will be designed in collaboration with assessors and learners to ensure that they remain relevant and useful beyond the project completion. 


The student would become part of a world-class group on human-centred artificial intelligence, optimisation and decision-making, with other PhD students working on a variety of contexts, for example, education technology, steelmaking, and coastal engineering. They will have access to a world-leading high-performance computing cluster, with a dedicated 36-core node for their research.   

In addition, they will be a part of the wider cohort starting in October at the EPSRC EPIC CDT (Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems). They will be based at the Centre in the Computational Foundry at Swansea University and will be provided group opportunities such as training in essential skills, and invited to industry talks and cohort research retreats.

Culture and environment   

Our group at Swansea is ambitious, consisting of motivated individuals. We are optimistic about the future and hope to contribute to the betterment of the world. Most importantly, we value human characteristics such as humility, kindness, empathy, and passion, and we are committed to fostering a supportive and inclusive environment. We aim to enable our group members to reach their full potential irrespective of their background.   

We strongly encourage you to reach out to some of our current PhD students to get a vibe of the environment, facilities and supervision culture. For this particular project, prospective students may contact Mr Andy Gray (, a current PhD student. 

For more details please see here:

Entry Requirements

Candidates must hold an Upper Second Class (2.1) honours degree or an appropriate master’s degree with a minimum overall grade at ‘Merit’ in Computer Science, Mathematics or a closely related discipline. 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. 

English Language: IELTS 6.5 Overall (with no individual component below 6.0) or Swansea University recognised equivalent. Full details of our English Language policy, including certificate time validity, can be found here. 

Desirable skills and attributes: 

  • Excellent numerical and progamming skills;
  • Knowledge of Python.; 
  • Knowledge of Bayesian statistics, machine learning, and optimisation, or a willingness to learn.

This scholarship is open to candidates of any nationality.


Please note that the programme requires some applicants to hold ATAS clearance, further details on ATAS scheme eligibility are available on the UK Government website. 

ATAS clearance IS NOT required to be held as part of the scholarship application process, successful award winners (as appropriate) are provided with details as to how to apply for ATAS clearance in tandem with scholarship course offer. 

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


This scholarship covers the full cost of tuition fees and an annual stipend at £19,237.

Additional research expenses will also be available.

How To Apply

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

  1. Course choice – please select Computer Science / PhD / Full-time / 3 Year / October

    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 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 ‘RS590 - Elevating Comparative Judgement’

*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: 

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 Dr Alma Rahat (

*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.

Who is eligible to apply?

This scholarship is open to candidates of any nationality.

Swansea University Campus

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