University of Sheffield
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We are seeking an enthusiastic and self-motivated PhD student to join a collaborative project funded by EPSRC, working on an innovative healthcare technology.
Project Overview
Healthcare digital twins are virtual replicas that continuously assimilate patient data to provide personalised predictions and support clinical decisions. To enable the clinician to make decisions, an accurate knowledge model of the patients’ condition is required. This PhD will investigate how knowledge models can be built and maintained for healthcare applications. The project will explore the use of combining healthcare ontologies, data structures and graphical modelling methods to create knowledge models. These will be applied to build a knowledge model for a cardiovascular digital twin. Of particular interest are methods that support the clinical decision-making process so that patient outcomes can be improved. In collaboration with clinicians, the new knowledge model methods will be used to propose improved patient-care pathways for pulmonary arterial hypertension.
Why Choose Us?
- Funded PhD at the standard EPSRC rate covering fees and bursary.
- Make an impact: Be at the forefront of novel healthcare technology, contributing to a balanced future.
- World-class environment: Develop your research expertise in a world-class research group.
- Interdisciplinary experience: Gain valuable experience working in an interdisciplinary environment.
Requirements:
- First-class or 2.1 honors degree (or international equivalent) in engineering, computer science, applied mathematics, or a related field.
Interested?
Contact Prof. David Wagg ([email protected]) for more information.
About the Research Environment:
The Dynamics Research Group in the School of Mechanical, Electrical and Civil Engineering at the University of Sheffield is one of the top tier dynamics groups in the World with 15 academic staff and over 50 PhD students. The Group covers a wide range of fundamental and applied research and has a large group of collaborators. You will be joining a great team of supportive and social PhD students working in a high-quality research environment.
Learn More:
The Dynamics Research Group:
Digital Twinning Interest Group:
https://www.sheffield.ac.uk/machine-intelligence/community/interest-groups/digital-twinning
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