PhD Studentship in Computer Science in AI and Machine Learning Explainability for Different Audiences

Award Summary

100% home fees covered, and a minimum tax-free annual living allowance of £21,805 (2026-27 UKRI rates)

Overview

One of the main issues with machine learning algorithms is that they are black boxes to human users. It is difficult to make sense of why a machine learning approach has provided a particular result as output. In this project, students will create explainable systems for machine learning systems. Through visualisation techniques, we will help open the black box of the model to understand training and/or predictions. This area, broadly speaking, is the target and applicants could help shape the project in this domain.

In this topic we are particularly interested in explaining machine learning methods to audiences to support explainability in these tasks where the user community is undertaking tasks outside of computer science and machine learning (for example, medicine). The focus will be on visualisation systems to support the understanding of the machine learning decisions.

Number Of Awards

1

Start Date

September 2026

Award Duration

3.5 years

Sponsor

School of Computing, Newcastle University

Supervisors

Professor Daniel Archambault

Eligibility Criteria

You must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in a relevant subject or subject relevant to the proposed PhD project. Enthusiasm for research, the ability to think and work independently, excellent analytical skills and strong verbal and written communication skills are also essential requirements.

Experience in machine learning and visualisation is desired.

The studentship covers fees at the Home rate (UK and EU applicants with pre-settled/settled status and meet the residency criteria). International applicants are welcome but must cover the difference between Home and International fees.

Applicants whose first language is not English require an IELTS score of 6.5 overall with a minimum of 5.5 in all sub-skills.