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Designing a pervasive framework for improving medical data explainability and interoperability

Project Description

The rapid advances in the field of AI (Artificial Intelligence) in recent years have seen its latest expansion to the healthcare domain generating enormous passions and interests from both the industry and the academia. AI in healthcare explores the use of complex machine learning algorithms to emulate human cognitive functions in the healthcare practices. In this domain, one of the newest areas is the use of AI in interpreting medical imaging data to assist in diagnosis. Although high accuracy has been reported in several cases, a major concern over such AI implementation is the so-called “black box” problems where the machine learning algorithm used in the AI system only delivers the results without telling why and how the process was done. In other words, as the machine learning algorithms are not designed with good explainability by nature, the corresponding algorithmic process of the AI system is not transparent enough to its end users leading to trust issues. There are currently several approaches to address this issue and this project aims to design a pervasive analysis and prediction framework for improving medical data explainability and interoperability.

How to apply:

Applications are made via our website using the Apply Online button below. If you have an enquiry about this project please contact us via the Email NOW button below, however your application will only be processed once you have submitted an application form as opposed to emailing your CV to us.

Candidates for a PhD Studentship should demonstrate outstanding qualities and be motivated to complete a PhD in 4 years and must demonstrate:

• Outstanding academic potential as measured normally by either a 1st class honours degree (or equivalent Grade Point Average (GPA) or a Master’s degree with distinction or equivalent
• An IELTS (Academic) score of 6.5 minimum (with a minimum 6.0 in each component, or equivalent) for candidates for whom English is not their first language and this must be evidenced at point of application.

The candidate must have good programming skills and experience in using statistical and analytical tools such as Matlab, R and Python etc.

The candidate must be able to take necessary research related travels.

Funding Notes

Funded candidates will receive a maintenance grant of £15,225 per year to contribute towards living expenses during the course of your research, as well as a fee waiver for 36 months.
Funded Studentships are open to both UK/EU and International students unless otherwise specified.

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