This project aims to develop software approaches to identify biomarkers for secondary infections in COPD using non-invasive exhaled breath samples. Using this approach, we aim to identify specific biomarkers that can be used to predict/diagnose the onset of a secondary infection prior to the patient becoming symptomatic as part of routine clinical monitoring. With this approach we hope to enable proactive clinical management of COPD patients through the early detection of secondary infections to avoid exacerbations of COPD which can lead to hospitalisation.
Chronic obstructive pulmonary disease (COPD) is characterised by long-term breathing problems which can be exacerbated by secondary infections. Current diagnosis of secondary infections in COPD patients requires invasive sampling which makes routine screening labour intensive, unpleasant and impractical for patients. Two schools at the University of Lincoln, School of Computer Science and School of Life Sciences are collaborating to develop software approaches to identify biomarkers for secondary infections in COPD using non-invasive exhaled breath samples.
The main aim of the PhD project is to develop algorithms for analysis of mass spectrometry (MS) data that can detect infection in COPD from non-invasive breath samples. You will analyse MS data exploiting advanced pattern recognition, mass spectrometry imaging, and machine/deep learning techniques for infection classification and prediction.
The successful student will be associated with the Laboratory of Vision Engineering in School of Computer Science, but will also be part of a cross-discipline collaboration amongst several research groups including Laboratory of Vision Engineering, Machine Learning group, Diabetes, metabolism and inflammation research group and Animal Behaviour and Welfare group. By joining the Doctoral Training Partnership programme at the University of Lincoln, you will be also conducting research together with other PhD and post-docs in a supportive and intellectually stimulating environment.
Skills the candidate will learn:
Whilst pursing this project, the student will gain extensive knowledge of the state-of-art of image processing, machine learning and deep learning techniques. They will develop advanced Matlab, Python or R programming skills, and obtain experience of cross-disciplinary working with academics, clinicians and other researchers. The student will also receive a broad training on research and transferable skills including scientific writing, presentation skills and project management.
Ideal candidates:
Have a First Class, Upper Second class (2:1) or Master qualification in Computer Science, Bioinformatics, or equivalent subject area
- Good knowledge of image processing or machine/deep learning
- Good programming skills of Matlab, Python, or R programming
- Capability to work independently and as part of a team
- Good mathematical background
- Excellent written and oral communication skills in English
- A real passion and commitment for research
Who is eligible for funding?
Please make sure to check the eligibility criteria before you apply. Normally, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. UK students will be eligible for a full studentship, covering the costs of Home fees, and a stipend to support living costs for 3.5 years.
Although most DTP students must be UK residents, we also have an opportunity for an international (EU and non-EU) student. The international studentship award will be subject to eligibility, and also the availability of complementary funding (to provide the differential to the international fee rate). You should get in touch with the lead supervisor before applying this award.
Application
To apply, please complete the application form and send it to [Email Address Removed]
Continue with Facebook
