Human-in-the-Loop Adaptive Learning for Trustworthy Artificial Intelligence

Summary

Artificial intelligence is increasingly used to support decision-making in fields such as healthcare, cybersecurity, finance, and public services.

However, many existing AI systems remain difficult to interpret and require vast amounts of data to perform well.

This PhD will investigate how AI can become more adaptable, efficient, and understandable by integrating people directly into the learning process.

The research will develop a Human-in-the-Loop Adaptive Learning (HILL) framework that combines active learning, reinforcement learning with human feedback, and explainable AI techniques.

The aim is to create models that learn effectively from smaller or incomplete datasets by drawing on selective human guidance to correct uncertainty and improve performance.

Such an approach can achieve high accuracy with less data while ensuring that decisions remain transparent and open to human oversight, qualities that are vital in digital health and other safety-critical applications.

The successful candidate will join the Intelligent Systems Research Centre (ISRC) at Ulster University’s Magee campus, working with experienced researchers in machine learning and cognitive analytics.

The candidate will gain practical skills using tools such as PyTorch, TensorFlow, and SHAP/LIME for model interpretation, alongside training in data ethics and responsible AI design.

This PhD will suit applicants with a background in computing or data science who wish to connect technical innovation with human-centred system design.

The project supports Northern Ireland’s priorities in Software/Cyber and Life & Health Sciences, contributing to the development of secure, transparent, and trustworthy AI systems that perform reliably even where data is limited.

Skills required of the applicant:

Applicants should ideally have a strong background in Computer Science, Artificial Intelligence, or a closely related discipline.

Experience with machine learning frameworks such as PyTorch or TensorFlow, and a good understanding of Python programming and data analysis, would be advantageous.

Knowledge of reinforcement learning, active learning, or explainable AI is desirable but not essential, as training will be provided.

Excellent analytical thinking, problem-solving skills, and the ability to work independently and collaboratively within an interdisciplinary research environment are also required.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain
• A demonstrable interest in the research area associated with the studentship

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings
• Use of personal initiative as evidenced by record of work above that normally expected at career stage.

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)

This scholarship will cover tuition fees and provide a maintenance allowance of £21,000* (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of approximately £900 per annum is also available.

To be eligible for these scholarships, applicants must meet the following criteria:

• Be a UK National, or

• Have settled status, or

• Have pre-settled status, or

• Have indefinite leave to remain or enter, or

• be an Irish National

Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies.

*Part time PhD scholarships may be available, based on 0.5 of the full time rate, and will require a six year registration period