Doctor using AI

Enhancing Healthcare Applications Through Cost-effective AI Solutions

Eligibility: UK/International (including EU) graduates with the required entry requirements

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 25 April 2024

Interview date: Will be confirmed to shortlisted candidates

Start date: September 2024

For further details, contact: Prof Dingchang Zheng, Professor and Centre Director, Research Centre for Intelligent Healthcare, Coventry University


This is a four year collaborative studentship which requires the candidate to spend two full years based at Coventry University (UK) and two years based at A*STAR Research Institute (Singapore). The usual pattern is first and fourth years at Coventry and second and third year at A*STAR Research Institute.

In the context of the United Kingdom's national healthcare agenda, the strategic framework for AI-based Intelligent Healthcare is a pivotal initiative. While intelligent healthcare research predominantly emphasizes supervised learning with a reliance on extensive annotated data points, a significant challenge arises in many applications due to the impracticality of obtaining such annotated data. This challenge is exacerbated by the inherent ambiguity and uncertainty in the labeling process, rendering the conventional approach economically burdensome.

Project details

The objective of this project is as follows: 1) Develop advanced deep active learning (DAL) algorithm to address the challenges posed by conventional methods; 2) Combine DAL with domain adaptation to reduce the annotation cost even further; 3) apply these algorithms to two healthcare applications: Uterine Contraction Identification and Cuffless Blood Pressure Estimation. This research marks a significant stride towards cost-effective AI, presenting a practical and impactful approach to advancing healthcare through innovative machine learning methodologies.

We seek a highly-talented, motivated, and open-minded candidate who are proficient in deep learning and transfer learning methodologies, with a strong foundation in advanced mathematical concepts. A strong background in signal processing would be desirable.


Coventry University and A*STAR jointly offer a fully-funded PhD studentship including tuition fees and stipend/bursary, that is open to both UK/EU and international graduates as part of the A*STAR Research Attachment Programme (ARAP).

Coventry University and A*STAR will only cover the stipend up to a maximum of two years each. Changes to the mobility pattern will only be considered under exceptional circumstances and can impact on the duration of the course and level of funding available. Should a candidate request any changes to mobility which results in the period spent in either the UK or Singapore extending beyond two years then the candidate is responsible for covering the stipend for that period.


The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

Entry requirements

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.


  • The potential to engage in innovative research and to complete the PhD within a 3.5 years.
  • A minimum of English language proficiency (IELTS academic overall minimum score of 7.0 with a minimum of 6.5 in each component).

How to apply

To find out more about the project, please contact Prof Dingchang Zheng, Professor and Centre Director, Research Centre for Intelligent Healthcare, Coventry Universitys

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

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