AI for Digital Medical Technologies

Summary

Ulster University School of Computing is offering the following three UKRI Future Medicine Institute-FMI PhD Studentships.

These studentships are funded via the UKRI Strength in Places Programme and will last three years.

The Future Medicines Institute (FMI) is a £55 million industry-led hub and it aims to advance Northern Ireland's medical devices and precision medicine sector by bringing together academics, clinicians, and leading health-science companies to accelerate the development of products.

Although led by QUB , Ulster University are leading a Digital Medical Technology element of the programme and the following PhDs will be based at the Ulster University Belfast Campus.

The work supports the Life & Health Sciences and Software/Cyber DfE sectoral priorities and aligns with the Centre for Digital Healthcare Technology, part of the Belfast Regional City Deal.

PhD researchers will join a vibrant research community exploring how AI can transform Digital Medical Technologies, enhance patient safety, and build public trust in emerging digital systems.

We welcome applicants who are interested in projects in the following areas:

Optimisation of Agentic Workflows for new Digital Medical Technologies

Supervisor names:  Chris Nugent,  Shuai Zhang, Sally McClean, Sumi Helal (external)

The aim of this Project is to investigate computational approaches that can be developed to optimise the agentic workflow of the design, development and deployment of digital medical technologies.

This will involve the optimisation of human resource,  equipment usage and ordering, and the physical environment.

It is envisaged that the main computational approaches to be considered will be based on agentic technologies and the Digital Twin paradigm.

The contributions to knowledge are expected to be related to the optimisation algorithms developed to offer the enhanced models for agentic workflow management and the ability of the Digital Twin to work in application agnostic environments whereby requirements capture can be automated and used as input to the Digital Twin.

Evaluating the effectiveness of domain specific language models in support of Reg Tech

Supervisor names: Chris Nugent,  Shengli Wu,  Shuai Zhang, Sumi Helal (external)

The aim of this research is to investigate the utility and effectiveness of domain specific language models in the assessment of documentation produced during digital medical technologies research and development.

Outputs from the process will have the ability to present risk based recommendations of on areas of the documentation that may require further attention prior to submission for regulatory approval.

It is anticipated that in the first instance the regulatory standards of ISO13845,  ISO14971 and ISO62304 will be considered.

The contributions to knowledge are expected to be related to the enhancement of the underlying language models to offer improved performance, reduced data leakage and provision of safety critical operating environments.

Time series analysis in the forecasting of trends in digital medical technologies

Supervisor names: Chris Nugent,  Rashid Kamal,  Shuai Zhang, Sumi Helal (external)

The aim of this Project is to investigate time series analysis approaches that can be developed to support the forecasting of trends in the applications of Digital Medical Technologies.

Central to the underpinning research will be the ability to develop a novel data and knowledge driven approach to forecasting.  This will leverage the ability of natively fine tuned large language models to analyse data from a range of both internal and external sources .

In addition, research will be undertaken to examine the possibility of developing forecasting approaches with the ability to be influenced by a range of externally dependent anomalies.

Applying to Multiple Projects: Applications for more than one PhD studentship are welcome, however if you apply for more than one PhD project, your first application on the system will be deemed your first-choice preference and further applications will be ordered based on the sequential time of submission.

If you are successfully shortlisted, you will be interviewed only on your first-choice application and ranked accordingly. Those ranked highest will be offered a PhD studentship.

In the situation where you are ranked highly and your first-choice project is already allocated to someone who was ranked higher than you, you may be offered your 2nd or 3rd choice project depending on the availability of this project.

The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education.  We particularly welcome female applicants, as they are under represented within the school.

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.

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.

• Masters at 65%
• Work experience relevant to the proposed project
• Publications - peer-reviewed

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:

• Future Medicines Institute (FMI)

Tuition fees will be covered in full and a stipend of £21,805 per annum will be paid for three years (subject to satisfactory academic performance).

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.