Responsible AI for Critical Systems: Interpretable, Auditable and Real-Time Machine Learning under Real-World Constraints

Summary

Artificial intelligence (AI) is increasingly required to inform decisions in critical systems, where an error is costly, an unexplained output is unusable, and the answer is often needed within microseconds or under strict governance. This thematic PhD pursues one question across three different domains: how do we build machine learning that is interpretable, auditable and efficient in automated decision-making?

The candidate will pursue one of the three complementary strands, each sharing a methodological core of responsible AI:

Strand 1:

“An Intelligent Multimodal Data Fusion and Predictive Modelling Framework for Early Detection of Neurodegenerative Diseases”, led by Dr Xuemei Ding, develops an AI framework for early diagnosis and risk prediction by fusing clinical records, cognitive assessments, medical imaging, physiological signals and lifestyle data, with clinical expertise and data support provided by a collaborating neurologist. The emphasis is on models that are accurate and efficient, but also interpretable and ethical enough to support evidence-based clinical decision-making.

or Strand 2:

“A Trustworthy AI-Assisted Digital Twin for Climate-Resilient Planning in Belfast”, led by Dr Vimal Dwivedi, builds a versioned decision-support digital twin of selected Belfast neighbourhoods, integrating geospatial, environmental, infrastructure, social and planning data so that green-blue infrastructure options can be compared. A deliberately bounded agentic AI assistant will help users locate approved evidence and produce plain-English, source-linked explanations, without accessing the open web, altering authoritative data or making planning decisions. Belfast City Council officers retain control throughout.

or Strand 3:

“FastML for Scalable and Fault-Tolerant Quantum Computing”, led by Dr Barry Dillon, investigates how fast machine-learning techniques can enable scalable and fault-tolerant quantum computing, with applications in qubit readout, quantum error correction, noise modelling and anomaly detection, where accurate decisions must be made under strict latency and resource constraints. It will use hls4ml to translate models into efficient FPGA implementations for real-time deployment close to quantum-control hardware, and explore the co-design of algorithms and specialised hardware.

Across the strands the research addresses heterogeneous, high-dimensional and longitudinal data; latency- and resource-constrained inference; uncertainty quantification; and AI assurance with meaningful human oversight. Depending on the selected strand, candidates may gain expertise in areas such as ML/DL, hardware-aware model design, multimodal data fusion, interpretability and human-centred evaluation.

Applicants should have a strong quantitative background, for example in computer science, machine learning, data science, engineering, physics or mathematics, and an interest in making AI trustworthy within critical systems.

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

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.

• Applicants should hold, or expect to hold a First Class Honours Degree in a relevant subject area
• International applicants should hold, or expect to hold a relevant Masters Degree at a level equivalent to a distinction at Ulster
• Work experience relevant to the proposed project
• Track record of peer reviewed publications
• Experience of presentation of research findings

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)
• Vice Chancellor's Research Scholarship (VCRS)

PhD Scholarships are available for this project and will cover:

• Tuition fees.
• A maintenance allowance of approximately £22,000 (exact rate is to be confirmed) per annum for three years, subject to satisfactory academic performance.
• A Research Training Support Grant (RTSG) of approximately £900 per annum.

To be eligible for these scholarships applicants must: be a UK or Irish national; or have pre-settled or settled status; or have indefinite leave to remain or enter.

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 i.e. September 2027.

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, funded from any source, including self-financing students, are NOT eligible to apply for an award.

Students in full-time employment are not eligible for an award.

Due consideration should be given to financing your studies.

Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.

The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:

1. Department for the Economy (DfE) PhD Scholarships
2. Vice Chancellor’s Research Scholarships (VCRS)

Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.