Applications are invited for a PhD studentship funded by Loughborough University to start in October 2016. The project will be based in the Department of Aeronautical and Automotive Engineering at Loughborough University.
There is an increasing need to deliver efficient and reliable patient care in the community. With a growing elderly population the number of people with chronic conditions is also continuously increasing. This puts pressure on an already stretched health service and has led to an increase in research into the development of reliable and efficient technology that supports self-care. Such technology would be required to receive multiple inputs from a variety of sources, process that information, make a decision or judgement and then initiate actions.
In this project models will be developed of such technology adapting the techniques that have been adopted for the study of engineering systems. The models will investigate the performance of the systems and the optimal system configuration. This will require accounting for the patient needs, for example the use of minimally invasive sensors. Applicants will be expected to have a strong background in mathematics, engineering or computer science to enable them to develop the integrated intelligent technologies for decision support in this healthcare arena.
General information about the Department of Aeronautical and Automotive Engineering can be found at: http://www.lboro.ac.uk/departments/aae/
For informal enquiries about the project, please contact Dr Sarah Dunnett: [email protected]
or Dr Lisa Jackson: [email protected]
The studentship is for 3 years and is intended to start October 2016.The studentship provides a tax free stipend of £14,296 per annum for the duration of the studentship plus tuition fees at the UK/EU rate. Please note that due to funding restrictions only students with a UK/EU fee status will be considered for this position.
Students will normally need to hold, or expect to gain, at least a 2:1 degree (or equivalent) in Engineering, Mathematics or Computer Science. A relevant Master’s degree and/or experience in one or more of the following will be an advantage: mathematical modelling, reliability modelling.