Loughborough University is a top-ten rated university in England for research intensity (REF2014) and an outstanding 66% of the work of Loughborough’s academic staff who were eligible to be submitted to the REF was judged as ‘world-leading’ or ‘internationally excellent’, compared to a national average figure of 43%. In choosing Loughborough for your research, you’ll work alongside academics who are leaders in their field. You will benefit from comprehensive support and guidance from our Doctoral College, including tailored careers advice, to help you succeed in your research and future career. Bone fracture affects millions of people’s life worldwide. In UK alone, there are approximately more than 850,000 new fractures each year, and the estimated annual cost for bone fracture related treatment reached more than £2 billion. Traditional methods of fracture treatment involve the use of internal/external fracture fixation and/or surgical intervention. An emerging field of tissue engineering enables a new paradigm of treatments, using engineered tissue scaffolds and constructs for repairing or replacing damaged tissue. In addition, shift in demographic population and aging related degenerative bone diseases such as osteoporosis can alter properties of bone tissue and result in a detrimental effect on the outcome of such treatments. Therefore, understanding the structure-function relationship of bone tissue in a wider population and disease conditions is key to the success for early diagnosis and prognosis of bone fracture and the subsequent healing process. The proposed project aims to develop a novel multi-scale, population-based approach to investigate damage and fracture mechanisms of bone tissue using the latest experimental and computational techniques, such as quantitative computer-tomography, high-performance computing and finite element analysis. The project is built upon previous research completed at Loughborough University, in collaboration with leading clinicians and scientists in the field with an aim to further advance the capacity and accuracy of in-silico models for various patient groups and loading conditions.