Lightweight Deep Learning in internet of medical things (IoMT)

These projects are open to students worldwide, but have no funding attached. Therefore, the successful applicant will be expected to fund tuition fees at the relevant level (home or international) and any applicable additional research costs. Please consider this before applying.

In a world where we have needed to be connected but apart, the need for enhanced remote and at-home healthcare has become clear. The Internet of Things (IoT) offers a promising solution. The IoT has created a highly connected world, with billions of devices collecting and communicating data from a range of applications, including healthcare. Due to these high volumes of data, a natural synergy with Artificial Intelligence (AI) has become apparent - big data both enables and requires AI to interpret, understand, and make decisions that provide optimal outcomes.

In this study, we aim at leveraging the advances in machine learning, and particularly deep learning, to further improve the analysis of health and medical data. We plan a two-stage approach: 1) The deployment of IoMT digital platform, and 2) lightweight AI model development. This PhD project support distance learning.

Informal enquiries can be directed to Dr Dewei Yi (dewei.yi@abdn.ac.uk) if the topic within this project is of interest to you, he is happy to discuss further with interested candidates.

Decisions will be based on academic merit. The successful applicant should have, or expect to obtain, a UK Honours Degree at 2.1 (or equivalent) in Computer Science, Robotics, Electrical, Electronic, Mechanical engineering or related fields. A relevant Master’s degree and/or experience in one of the above will be an advantage.

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.

Application Procedure:

Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php.

You should apply for Degree of Doctor of Philosophy in Computing Science to ensure your application is passed to the correct team for processing.

Please clearly note the name of the lead supervisor and project titleon the application form. If you do not include these details, it may not be considered for the project.

Your application must include: A personal statement, an up-to-date copy of your academic CV, and clear copies of your educational certificates and transcripts.

Please note: you do not need to provide a research proposal with this application.

If you require any additional assistance in submitting your application or have any queries about the application process, please don't hesitate to contact us at researchadmissions@abdn.ac.uk

Funding Notes

This is a self-funding project open to students worldwide. Our typical start dates for this programme are February or October.

Fees for this programme can be found here Finance and Funding | Study Here | The University of Aberdeen

References

Yi, Dewei, Petar Baltov, Yining Hua, Sam Philip, and Pradip Kumar Sharma. "Compound Scaling Encoder-Decoder (CoSED) Network for Diabetic Retinopathy Related Bio-marker Detection." IEEE journal of biomedical and health informatics (2023).