|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||£15,225 Maintenance grant per annum|
|Placed On:||13th March 2020|
|Closes:||11th May 2020|
Lead Supervisor name: Dr. Nan Jiang
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.
What does the funded studentship include?
Funded candidates will receive a maintenance grant of £15,225 per annum (unless otherwise specified), to cover their living expenses and have their fees waived for 36 months. In addition, research costs, including field work and conference attendance, will be met.
Funded Studentships are open to both UK/EU and International students unless otherwise specified.
Candidates for a PhD Studentship should demonstrate outstanding qualities and be motivated to complete a PhD in 4 years and must demonstrate:
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.
In addition to satisfying basic entry criteria, BU will look closely at the qualities, skills and background of each candidate and what they can bring to their chosen research project in order to ensure successful and timely completion.
Closing date: The first call for applications will close on 11 May 2020.
For further information on how to apply click the ‘Apply’ button below or email firstname.lastname@example.org
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