Funding
Funded
Addressing Challenges of Multi-Modal Data Analysis in Application to Precision Medicine
School of Electronics, Electrical Engineering and Computer Science | PHD
Reference Number
EEECS/2025/AJL1
Application Deadline
28 February 2025
Start Date
1 October 2025
Overview
Precision medicine aims to tailor healthcare interventions based on individual differences in patients' genetic, environmental, and lifestyle factors. This personalized approach has gained momentum through the availability of diverse biomedical data modalities, which provide comprehensive insights into human health and disease. Examples of biomedical data modalities include: genomic data (high-dimensional sequences representing genetic information), imaging data (rich visual information from modalities like MRI or CT scans, often with a large spatial resolution but comparatively low feature dimensionality), clinical records (longitudinal data capturing patients' medical histories, treatment plans, and outcomes, generally unstructured and variable in length). Multi-modal data analysis, which combines various types of data, is critical for advancing precision medicine as it can reveal complex patterns and relationships across datasets that, when analysed individually, might fail to yield actionable insights. For instance, fusing imaging data with genomic information can enhance cancer diagnosis by linking tumour phenotypes to specific genetic mutations, while combining patient records with proteomic profiles can facilitate more precise predictions of treatment responses. Given these diverse data sources, combining them effectively in a machine learning (ML) model is crucial yet fraught with several major difficulties.
This project will investigate the following three challenges:
1. Optimal Fusion of Different Data Modalities
A fundamental challenge in multi-modal data analysis is determining the most effective fusion strategy to combine information from different modalities to enhance the performance of ML models. Given the varied dimensionalities, scales, and information content across modalities, establishing optimal fusion techniques is critical to balance the significance of each data source in the model. Understanding the relationships between modalities is essential; for example, certain genomic markers may correlate with specific imaging features, creating a synergistic effect that can lead to better predictions in disease diagnosis or treatment response. However, aligning and harmonizing these diverse features is complex, as models must be designed to handle both high-dimensional data (e.g., genomics) and relatively low-dimensional data (e.g., clinical features) effectively.
2. Explainability in ML Models Trained on Multi-Modal Data
In precision medicine, model interpretability is crucial to ensure that healthcare professionals can trust and understand ML-driven recommendations. However, multi-modal data introduces layers of complexity, as each modality may contribute uniquely to the predictions made by the model. Explainability methods that reveal how and why a model’s decision is influenced by each data type are necessary to avoid "black-box" models, which could limit clinical adoption. Addressing this challenge requires developing novel explainability frameworks capable of deconstructing predictions to show the contribution of each modality, ideally in a way that aligns with domain knowledge, making the model’s output interpretable and actionable.
3. Handling Imbalanced Multi-Modal Data
Imbalanced datasets, where certain classes or types of data are underrepresented, are common in precision medicine applications. For example, rare diseases may have limited genomic data available, while imaging data may be abundant. This imbalance can lead to biased models that overfit to the more prevalent data types, resulting in suboptimal generalization. Managing this imbalance across multiple modalities requires advanced data pre-processing, augmentation, or sampling techniques that can balance the data without compromising the integrity of the information from each modality.
Project Description:
The primary goal of this research project is to develop methodologies that address the outlined challenges, facilitating the use of multi-modal data in precision medicine to improve predictive accuracy, model interpretability, and robustness in real-world applications. Specifically, the objectives of the project are as follows:
1. Develop novel methods for optimal fusion of multi-modal data
• Investigate and develop fusion techniques that consider modality-specific characteristics, such as dimensionality and scale, to maximize the predictive power of ML models.
• Identify and model inter-modal relationships that can enhance feature representation and improve the generalizability of multi-modal ML models.
• Evaluate different fusion strategies (early, intermediate, late fusion) to determine optimal configurations for diverse precision medicine tasks, such as diagnostics, prognostics, and treatment response prediction.
2. Create explainability frameworks for multi-modal ML models
• Design explainability techniques tailored for multi-modal data that provide insights into the contribution of each modality to the model’s decision-making process.
• Integrate explainability mechanisms that make the model’s outputs understandable and actionable for clinical professionals, thereby increasing the potential for clinical adoption.
• Evaluate these frameworks on real-world healthcare datasets to measure the degree of interpretability and transparency they afford to end users.
3. Develop strategies to manage imbalanced multi-modal data
• Investigate data augmentation and resampling techniques that address class imbalances across different modalities while preserving important modality-specific information.
• Create methods to ensure that minority data classes in one modality do not disproportionately affect the model's overall learning process, thus improving robustness and fairness.
• Test these approaches on datasets with known imbalances (e.g., rare disease cohorts) to validate improvements in model performance and generalization.
Funding Information
To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.
To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:
• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.
Candidates from ROI may also qualify for Home student funding.
Previous PhD study MAY make you ineligible to be considered for funding.
Please note that other terms and conditions also apply.
Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.
A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.
Academic Requirements:
The minimum academic requirement for admission is normally an Upper Second Class Honours 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: 3 or 3.5 years
Funding Body
Funding TBC
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 | TBC |
| Republic of Ireland (ROI) 2 | TBC |
| England, Scotland or Wales (GB) 1 | TBC |
| 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.