Funding
Funded
Advanced AI Methods for Analysing Nutritional Influences on Mental Health
School of Electronics, Electrical Engineering and Computer Science | PHD
Reference Number
EEECS/AJL/NILAB
Application Deadline
14 April 2025
Start Date
1 October 2025
Overview
This project is a 4-year PhD project with enhanced training and 3+ month placement, which is funded by UKRI BBSRC, delivered by Queen’s University Belfast and Ulster University. Mental health conditions, such as depression and anxiety, affect millions globally, creating an urgent need for innovative strategies to improve care. While the impact of nutrition on brain function and mental well-being is increasingly recognized, uncovering how dietary patterns interact with genetic and pharmacological factors remains a challenge. This project seeks to tackle this complexity by combining machine learning (ML) and topological data analysis (TDA) to uncover new insights into the links between nutrition, genetics, and mental health. TDA is a modern mathematical approach that uncovers patterns in complex, high-dimensional datasets. This enables researchers to identify global structures, such as clusters of individuals with similar mental health trajectories, and subtle, localized patterns, like specific dietary-genetic interactions. When paired with ML, which excels at uncovering predictive relationships, this project aims to create powerful models that inform tailored, evidence-based mental health interventions.
Students joining this project will become a part of innovative multidisciplinary effort at Queen’s University Belfast, exploring how data-driven approaches can unlock new solutions for mental health challenges, ultimately improving lives through personalized, science-backed care. The project will be run between the School of Electronics, Electrical Engineering and Computer Science and the Centre for Public Health. Students will benefit from a multidisciplinary training program designed to expand their skillsets. Those with backgrounds in health or bioinformatics will learn advanced AI, ML, and TDA techniques, while students from computing or mathematics will gain expertise in nutritional science, gene expression analysis, and epidemiology. This tailored approach ensures all participants are equipped to contribute meaningfully to this transformative research.
Cognitive and mental health conditions, such as anxiety and depression, affect millions globally and represent a significant public health burden. Nutrition has been increasingly recognized as a critical factor influencing brain function, neurotransmitter balance, and overall mental well-being [1, 2]. However, identifying specific dietary patterns and their interplay with genetic, biological, and environmental factors that positively or negatively impact mental health is challenging due to the complexity of these influences and the individual variability they present. A deeper understanding of these relationships—considering both nutritional and biological dimensions—could revolutionize mental health care by enabling personalized interventions that integrate dietary, pharmacological, and genetic approaches.
This project will leverage machine learning (ML) [3] and topological data analysis (TDA) [4] to analyse and interpret the complex interplay between genetic, nutritional, and pharmacological factors in mental health. TDA, a field rooted in computational topology, provides tools to uncover intrinsic patterns, structures, and shapes within high-dimensional datasets. It is particularly well-suited to extracting insights from heterogeneous and noisy data, such as the intricate relationships formed by dietary habits, genetic predispositions, environmental exposures, and mental health outcomes. By examining the topology of the data, TDA enables the discovery of global structures—such as clusters of individuals with similar health trajectories—and local features, such as subtle and context-dependent subgroups based on specific genetic and dietary interactions.
Machine learning complements TDA by providing powerful algorithms to identify and predict relationships within large, multifaceted datasets. ML models excel in detecting non-linear interactions and high-order dependencies, making them ideal for uncovering hidden connections between dietary patterns, genetic variations, and pharmacological treatments. Integrating these methods enables the creation of predictive models that not only improve understanding of how these factors influence mental health but also provide actionable insights for personalized interventions.
The research will focus on the following specific objectives:
1. Topological Characterization of Dietary and Genetic Interactions
This objective will use TDA to identify persistent topological structures in dietary and genetic data that are associated with positive or negative mental health outcomes. Persistent features may reflect underlying biological processes or dietary habits critical for mental health. The topological structures will be interpreted and evaluated in collaboration with clinical and biological experts to ensure their plausibility and relevance for future hypothesis-driven studies.
2. Topological Fingerprints of Mental Health States
This objective will incorporate TDA and ML to define and validate topological "fingerprints" that differentiate between mental health states (e.g., depression, anxiety) based on combined dietary, genetic, and medication data. These fingerprints can act as biomarkers for diagnosis and treatment planning. The findings will be validated using real-world datasets and expert consultation.
3. Development of Personalized Interventions
This objective will incorporate TDA with ML to design integrated, personalized mental health recommendations that incorporate individualized topological profiles of dietary, genetic, and medication data. The rationale in here is that topological profiles could reveal unique, non-linear relationships that guide more targeted interventions. The applicability of the developed intervention models will be evaluated using real-world data.
By combining cutting-edge tools from computational mathematics and ML, this project will help to answer three crucial research questions:
1. What are the specific dietary patterns, nutrients associated with positive mental health outcomes?
2. How do dietary patterns and genetic factors interact with other variables, such as medication, lifestyle, and environmental influences, to impact mental health?
3. Can AI-driven analysis of these interactions lead to the development of integrated, personalized interventions that improve mental health outcomes?
Funding Information
Funding covers fees and stipend (stipend is £20,780 for the 2025/26 academic year).
Academic Requirements:
The minimum academic requirement for admission is normally an Upper Second Class Honours or above degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.
Project Summary
Supervisor
Dr Anna Jurek-Loughrey
Mode of Study
Full-time: 4 Years
Funding Body
BBSRC
Computer Science overview
The School of Electronics, Electrical Engineering and Computer Science (EEECS) aims to enhance the way we use technology in communication, data science, computing systems, cyber security, power electronics, intelligent control, and many related areas.
You’ll be part of a dynamic doctoral research environment and will study alongside students from over 40 countries world-wide.
We supervise students undertaking research in key areas of computer science, including:
- Artificial Intelligence
- Cybersecurity
- Computing Systems
- Power Electronics
- Robotics
- Sensor-based Systems
- Wireless Communications
Within the School we have a number of specialist research centres. As part of a lively community of over 100 full-time and part-time research students you’ll have the opportunity to develop your research potential in a vibrant research community that prioritises the cross-fertilisation of ideas and innovation in the advancement of knowledge.
Many PhD studentships attract scholarships and top-up supplements. PhD programmes provide our students with the opportunity to acquire an extensive training in research techniques.
Computer Science Highlights
Professional Accreditations
- ECIT brings together, in one building, internationally recognised research groups specialising in key areas of advanced digital and communications technology.
Industry Links
- Queen’s researchers have strong links with the local industry, which boasts a rich mix of local startups and multi-nationals. Belfast is the second fastest growing region in the UK in terms of Knowledge Economy activity (Northern Ireland Economy Report, 2018).
- CSIT brings together research specialists in complementary fields such as data security, network security systems, wireless-enabled security systems, intelligent surveillance systems; and serves as the national point of reference for knowledge transfer in these areas.
World Class Facilities
- The state-of-the-art £14m Computer Science Building and the Institute of Electronics, Communications and Information Technology offer bespoke research environments.
The Institute of Electronics, Communications and Information Technology (ECIT), with state-of-the-art technology, offers a bespoke research environment.
Internationally Renowned Experts
- You will be working under the supervision of leading international academic experts.
Key Facts
Research students are encouraged to play a full and active role in relation to the wide range of research activities undertaken within the School and there are many resources available including:
- A wide range of personal development and specialist training courses offered through the Personal Development Programme
- Access to the Queen's University Postgraduate Researcher Development Programme
- Office accommodation with access to computing facilities and support to attend conferences for full-time PhD students
Course content
Research Information
Associated Research
Research within the School is organised into research themes combining strengths by working together on major projects, in many cases in collaboration with key technology companies.
ECIT brings together internationally recognised research groups specialising in key areas of advanced digital and communications technology.
PhD Opportunities
PhD Opportunities are available in a wide range of computer science subjects, aligned to the specific expertise of our PhD supervisors.
Research Impact
Queen’s is a leader in commercial impact and one of the five highest performing universities in the UK for intellectual property commercialisation. We have created over 80 spin-out companies. Three of these -
Kainos, Andor Technology and Fusion Antibodies - have been publicly listed on the London Stock Exchange.
Research Projects
Queen’s has strong collaborative links with industry in Northern Ireland, and internationally. It has a strong funding track record with EPSRC and the EC H2020 programme.
Research Success
The research profile produced by the 2014 UK Research Excellence Framework (REF) graded 80 per cent of our research activity as 'world-leading' or 'internationally excellent', confirming the School's reputation as an internationally-leading department.
Career Prospects
Introduction
For further information on career opportunities at PhD level please contact the Faculty of Engineering and Physical Sciences Student Recruitment Team on askEPS@qub.ac.uk.
Our advisors - in consultation with the School - will be happy to provide further information on your research area, possible career prospects and your research application.
People teaching you
Course structure
There is no specific course content as such. You are expected to take research training modules that are supported by the School which focus on quantitative and qualitative research methods. You are also expected to carry out your research under the guidance of your supervisor.Over the course of study you can attend postgraduate skills training organised by the Graduate School.
You will normally register, in the first instance, as an ‘undifferentiated PhD student’ which means that you have satisfied staff that you are capable of undertaking a research degree. The decision as to whether you should undertake a PhD is delayed until you have completed ‘differentiation’.
Differentiation takes place about 8-9 months after registration for full time students and about 16-18 months for part time students: You are normally asked to submit work to a panel of up two academics and this is followed up with a formal meeting with the ‘Differentiation Panel’. The Panel then make a judgement about your capacity to continue with your study. Sometimes students are advised to revise their research objectives or to consider submitting their work for an MPhil qualification rather than a doctoral qualification.
To complete with a doctoral qualification you will be required to submit a thesis of approx 80,000 words and you will be required to attend a viva voce [oral examination] with an external and internal examiner to defend your thesis.
A PhD programme runs for 3-4 years full-time or 6-8 years part-time. Students can apply for a writing up year should it be required.
The PhD is open to both full and part time candidates and is often a useful preparation for a career within academia or consultancy.
Full time students are often attracted to research degree programmes because they offer an opportunity to pursue in some depth an area of academic interest.
The part time research degree is an exciting option for professionals already working in the education field who are seeking to extend their knowledge on an issue of professional interest. Often part time candidates choose to research an area that is related to their professional responsibilities.
If you meet the Entry Requirements, the next step is to check whether we can supervise research in your chosen area. We only take students to whom we can offer expert research supervision from one of our academic staff. Therefore, your research question needs to engage with the research interests of one of our staff.
Assessment
- Assessment processes for the Research Degree differ from taught degrees. Students will be expected to present write up their work at regular intervals to their supervisor who will provide written and oral feedback; a formal assessment process takes place annually.
This Annual Progress Review requires students to present their work in writing and orally to a panel of academics from within the School. Successful completion of this process will allow students to register for the next academic year.
The final assessment of the doctoral degree is both oral and written. Students will submit their thesis to an internal and external examining team who will review the written thesis before inviting the student to orally defend their work at a Viva Voce.
Feedback
- Supervisors will offer feedback on the research work at regular intervals throughout the period of registration on the degree.
Facilities
Full time PhD students will have access to a shared office space and access to a desk with personal computer and internet access.
Entrance requirements
Graduate
The minimum academic requirement for admission to a research degree programme is normally an Upper Second Class Honours degree from a UK or ROI HE provider, or an equivalent qualification acceptable to the University. Further information can be obtained by contacting the School.
International Students
For information on international qualification equivalents, please check the specific information for your country.
English Language Requirements
Evidence of an IELTS* score of 6.0, with not less than 5.5 in any component or equivalent qualification acceptable to the University is required (*taken within the last 2 years).
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
For more information on English Language requirements for EEA and non-EEA nationals see: www.qub.ac.uk/EnglishLanguageReqs.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Tuition Fees
| Northern Ireland (NI) 1 | £5,005 |
| Republic of Ireland (ROI) 2 | £5,005 |
| England, Scotland or Wales (GB) 1 | £5,005 |
| EU Other 3 | £25,600 |
| International | £25,600 |
1 EU citizens in the EU Settlement Scheme, with settled or pre-settled status, are expected to be charged the NI or GB tuition fee based on where they are ordinarily resident, however this is provisional and subject to the publication of the Northern Ireland Assembly Student Fees Regulations. Students who are ROI nationals resident in GB are expected to be charged the GB fee, however this is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
2 It is expected that EU students who are ROI nationals resident in ROI will be eligible for NI tuition fees. The tuition fee set out above is provisional and subject to the publication of the Northern Ireland Assembly student fees Regulations.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted are for the academic year 2021-22, and relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
More information on postgraduate tuition fees.
Computer Science costs
There are no specific additional course costs associated with this programme.
Additional course costs
All Students
Depending on the programme of study, there may also be other extra costs which are not covered by tuition fees, which students will need to consider when planning their studies . Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £100 per year for photocopying, memory sticks and printing charges. Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen. There are also additional charges for graduation ceremonies, and library fines. In undertaking a research project students may incur costs associated with transport and/or materials, and there will also be additional costs for printing and binding the thesis. There may also be individually tailored research project expenses and students should consult directly with the School for further information.
Bench fees
Some research programmes incur an additional annual charge on top of the tuition fees, often referred to as a bench fee. Bench fees are charged when a programme (or a specific project) incurs extra costs such as those involved with specialist laboratory or field work. If you are required to pay bench fees they will be detailed on your offer letter. If you have any questions about Bench Fees these should be raised with your School at the application stage. Please note that, if you are being funded you will need to ensure your sponsor is aware of and has agreed to fund these additional costs before accepting your place.
How do I fund my study?
1.PhD OpportunitiesFind PhD opportunities and funded studentships by subject area.
2.Funded Doctoral Training ProgrammesWe offer numerous opportunities for funded doctoral study in a world-class research environment. Our centres and partnerships, aim to seek out and nurture outstanding postgraduate research students, and provide targeted training and skills development.
3.PhD loansThe Government offers doctoral loans of up to £26,445 for PhDs and equivalent postgraduate research programmes for English- or Welsh-resident UK and EU students.
4.International ScholarshipsInformation on Postgraduate Research scholarships for international students.
Funding and Scholarships
The Funding & Scholarship Finder helps prospective and current students find funding to help cover costs towards a whole range of study related expenses.
How to Apply
Apply using our online Postgraduate Applications Portal and follow the step-by-step instructions on how to apply.
Find a supervisor
If you're interested in a particular project, we suggest you contact the relevant academic before you apply, to introduce yourself and ask questions.
To find a potential supervisor aligned with your area of interest, or if you are unsure of who to contact, look through the staff profiles linked here.
You might be asked to provide a short outline of your proposal to help us identify potential supervisors.