Behaviour Modelling in Digital Twins for the Identification of Mental Health Issues

Summary

Mental health of students in Higher Education have become an increasingly important topic and is of high priority for HE institutions. In the UK, it has been reported that mental health problems among university students have almost tripled in recent years. Between the 2016/17 and 2022/23 academic years, the proportion of undergraduate students at universities across the UK who reported to have experienced mental health difficulties rose from 6% to 16% (TASO2023).

Digital Twin technology, with their real-time and efficient data collection, integration and processing capabilities, precise analytical and predictive abilities, coupled with reliable decision-making support functions, have the potential to provide new perspectives for understanding and supporting the management of mental health challenges for students in HE settings. There is limited work in the literature on using digital twins within mental health, especially on the use of large-scale data to support the development and the evaluation of the proposed solutions.

A core and innovative compomemt of the Digital Twin is its ability to identify signature behaviour patterns which correspond to mental health issue. The PhD research is focused on the peronsalised behaviour modelling on the multifactorial variables in an effort to identify individuals with potential mental health issues based the observed behavioural patterns. Consideration will be given security issues on the model deployment in the Digital Twin solution, including data poisoning. The project will investigate solutions, including decentralized machine learning approaches to address such challenges. Furthermore, predictive behaviour modelling is presented with the challenge of significant intraclass variabilities. Given that the system is aimed to detect changes of behaviours compared to individual’s own baseline, the project aims to propose an innovative modelling methodology to use the characteristics of the behaviour changes as features, to enable better personalisation of predictive modelling for the Digital Twin of individuals.

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.

• Sound understanding of subject area as evidenced by a comprehensive research proposal
• 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

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)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £19,237 (tbc) per annum for three years (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE) and the Vice Chancellor’s Research Scholarships (VCRS), are open to applicants worldwide, regardless of residency or domicile.

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.