Research Fellow in Population Level Biomechanical Risk Assessment of Injury Associated to Vibration Exposure

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We are delighted to recruit for a Research Fellow to work on WBV-ACUTE which is part of the DASA initiative on understanding whole-body vibration (WBV) to predict and prevent future injury induced by exposure to WBV. WBV-ACUTE is a partnership between the School of Engineering, the Faculty of Medicine, and the Department of Electronics and Computer Science at the University of Southampton. You will be based at the University of Southampton, UK.

WBV-ACUTE will conduct a set of controlled trials on instrumented vehicles and personnel, fitted with dedicated sensor technology to understand the relationship between the vibration environment and personnel exposure more accurately. The vibration environment is then reproduced for a larger scale subjective testing on the University’s six axis motion simulator where short term effects are quantified in more detail while further epidemiological analyses will seek to explore patterns of injury and their relationships to WBV exposure.

Your role as Research Fellow will be to develop computational biomechanical models to perform subject-specific and population level analyses of the risk of mechanical overload and injury under and after exposure to shock and whole body vibration (WBV). Linking biomechanical analyses with essential insights on exposure levels and epidemiology will enable you to develop an understanding of biomechanical mechanisms to explain how WBV might subsequently lead to injuries of the spino-pelvic complex including the lumbar and cervical spine.

You will be working with Professor Markus Heller (Mechanical Engineering), Professor Neil Ferguson (ISVR), and collaborators Professor Elaine Dennison (MRC Lifecourse Epidemiology Centre) and Dr Ying Ye (ISVR). You need to have a PhD in Mechanical or Biomechanical Engineering or a related discipline. Experience in designing and conducting computational biomechanical analyses at the organ and tissue levels is essential while dedicated expertise in spinal biomechanics will give you an advantage. You will have a sound understanding of dynamics and strong skills in the image-based development of computational models and automation of analyses using e.g. MATLAB or Python. You will be comfortable working with statistical methods to quantify essential characteristics of complex data sets and ideally have practical experience in using machine learning approaches. In addition to undertaking original research that pushes the frontier of the state of the art, you will write and submit academic papers, prepare project deliverables, and travel to academic conferences and project meetings to present your work and represent the team.

As a university we aim to create an environment where everyone can thrive, and we are proactive in fostering a culture of inclusion, respect and equality of opportunity. We believe that we can only truly meet our objectives if we are reflective of society, so we are passionate about creating a working environment in which you are free to bring your whole self to work. With a generous holiday allowance as well as additional university closure days we are committed to supporting our staff and students and open to a flexible working approach.

Apply by midnight on the closing date. For assistance contact Recruitment on +44(0)2380 592750 or [email protected] quoting the job number.

We believe equality, diversity and inclusion are fundamental to making the University of Southampton a welcoming, vibrant and successful organisation. Having a diverse workforce, inclusive of people of all ages and beliefs, from different racial backgrounds, educational and social backgrounds opens up a wealth of possibilities, makes us more creative and accelerates our impact on society. We welcome applicants that value diversity of our community and are willing to play their part in supporting the mission of inclusivity. 

Please note that applications from agencies will not be accepted unless indicated on the job.