Improving outcomes in neurological conditions is impaired by limitations in diagnosis and monitoring. Optimising the diagnostic pathway can improve care through earlier treatment, while improved monitoring of the patient’s condition can enable clinicians to be proactive, rather than reactive, in their care.
Conditions which would benefit from improvements in diagnosis and monitoring include nerve/muscle diseases (such as motor neurone disease) and Parkinson’s disease (PD). Research into disease modifying therapies in these conditions has gained significant momentum in recent years and the need for improved markers of disease is greater than ever before.
Raman spectroscopy is a growing area of biomedical research due to its simplicity and versatility. The technique uses light from a narrow frequency laser to probe the chemical composition of a sample. As light interacts with molecules in the sample a small proportion of photons have their energy (or colour) changed. By collecting these photons and thus analysing the frequency content of the scattered light, insight is gained into the molecular bonds present in the sample. This information can be used to identify disease.
In this PhD the student will explore different a range of signal processing and machine learning techniques for the identification of neurological disease using Raman spectra collected from a range of patient samples, as well as preclinical models. They will gain training the collection of Raman data and in the application of advanced computational techniques to neurological diseases.
Entry Requirements:
Candidates must have a first or upper second class honours degree in maths, physics, engineering, computer science or related disciplines.
How to apply:
Please complete a University Postgraduate Research Application form available here: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying
Please clearly state the prospective main supervisor in the respective box and select 'Neuroscience' as the department.
Enquiries:
Interested candidates should in the first instance contact Dr James Alix, [Email Address Removed]
Closing date - Wednesday 26th January 2022 at 5pm.