Empowering Intelligent Healthcare Solutions: Privacy-Aware Learning in Neuromorphic Deep Networks

Summary

In the swiftly evolving field of healthcare, achieving precise disease diagnosis and effective remote patient monitoring is becoming increasingly vital, especially in light of growing data privacy concerns. Modern centralized machine learning algorithms are limited by privacy difficulties, latency challenges, and computational inefficiencies in handling large, distributed healthcare data. The project's purpose is to get accurate illness diagnosis and remote patient monitoring while safeguarding data security. The integration of Neuromorphic Computing (NC) with Federated Learning (FL) improves various facets, including the synthesis of medical data, patient diagnosis and monitoring, acceleration of pharmaceutical research, assistance for physicians in accurate illness diagnosis, and forecasting of treatment plans.

The proposed study will examine the potential applications of spiking neural networks (SNNs) and other advanced machine learning algorithms in medical diagnostics and the automated interpretation of radiographic images. The primary benefit of the FL model is the level of privacy provided to customers by keeping the training data at the user node. Training data is crucial for accurate model training, and there will be occasions where minimal information sharing suffices. Thus, the equilibrium between information dissemination and training precision necessitates meticulous evaluation to protect individual privacy.

The framework will encompass several essential components: first, the examination of biomedical imaging data to discern significant patterns linked to early disease indicators; second, the utilization of sophisticated machine learning algorithms to refine diagnostic procedures; and third, the deployment of a federated learning model to safeguard data privacy while improving model efficacy.

This study aims to utilize modern biomedical imaging and artificial intelligence for the early detection of chronic diseases, facilitating accessible healthcare solutions while safeguarding data privacy.

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.