Adaptive and Predictive Computational Modelling of Biomaterials: A Digital Twin Framework for Next-Generation Personalised Healthcare PhD 36 months PHD Program By Loughborough University |Top Universities

Program overview

Main Subject

Health/Healthcare

Degree

PhD

Study Level

PHD

Study Mode

On Campus

The healthcare industry is rapidly embracing digital technologies to deliver data-driven, personalised medicine. Digital twins—computer-based, or in silico, models that replicate physical objects or systems—provide a realistic environment for studying diseases, testing new drugs, and developing medical devices. This innovative approach accelerates life-saving advancements while significantly reducing costs.
Building on the proven success of our research group, this project aims to integrate cutting-edge multi-scale finite element models with advanced medical imaging techniques to establish a transformative paradigm in computational healthcare. The successful candidate will focus on implementing and validating these models using high-resolution in vivo tissue imaging data collected from leading bio-imaging centres.
You will be part of a multidisciplinary team led by Dr. Simin Li (School of Mechanical, Electrical, and Manufacturing Engineering) and Dr. Katherine Brooke-Wavell (School of Sport, Exercise, and Health Sciences). You will have access to world-class facilities within these schools, as well as imaging resources at the National Centre for Sport and Exercise Medicine.
The collaborative and multidisciplinary nature of this project ensures you will receive cutting-edge training, opportunities to engage with leading experts from both industrial and scientific fields, and preparation for diverse career paths in this rapidly evolving domain.
Loughborough University has an applied research culture. In REF 2021, 94% of the work submitted was judged to be top-rated as 'world-leading' or 'internationally excellent'. We are a community based on mutual support and collaboration. Through our Doctoral College there are continual opportunities for building important research skills and networks among your peers and research academics.

Program overview

Main Subject

Health/Healthcare

Degree

PhD

Study Level

PHD

Study Mode

On Campus

The healthcare industry is rapidly embracing digital technologies to deliver data-driven, personalised medicine. Digital twins—computer-based, or in silico, models that replicate physical objects or systems—provide a realistic environment for studying diseases, testing new drugs, and developing medical devices. This innovative approach accelerates life-saving advancements while significantly reducing costs.
Building on the proven success of our research group, this project aims to integrate cutting-edge multi-scale finite element models with advanced medical imaging techniques to establish a transformative paradigm in computational healthcare. The successful candidate will focus on implementing and validating these models using high-resolution in vivo tissue imaging data collected from leading bio-imaging centres.
You will be part of a multidisciplinary team led by Dr. Simin Li (School of Mechanical, Electrical, and Manufacturing Engineering) and Dr. Katherine Brooke-Wavell (School of Sport, Exercise, and Health Sciences). You will have access to world-class facilities within these schools, as well as imaging resources at the National Centre for Sport and Exercise Medicine.
The collaborative and multidisciplinary nature of this project ensures you will receive cutting-edge training, opportunities to engage with leading experts from both industrial and scientific fields, and preparation for diverse career paths in this rapidly evolving domain.
Loughborough University has an applied research culture. In REF 2021, 94% of the work submitted was judged to be top-rated as 'world-leading' or 'internationally excellent'. We are a community based on mutual support and collaboration. Through our Doctoral College there are continual opportunities for building important research skills and networks among your peers and research academics.

Admission Requirements

3.2+
6.5+
92+

31 Mar 2025
3 Years
Oct

Tuition fees

International
28,600

Scholarships

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