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Development and testing of deep learning algorithms to predict pulmonary exacerbations and development of antimicrobial resistance in people with chronic respiratory disease

  • Full or part time
    Dr M Tunney
  • Application Deadline
    Tuesday, March 31, 2020
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

In people with Cystic Fibrosis, acute infective exacerbations or ‘flare-ups’ of chronic respiratory infection are the single most important cause of poor outcome and are associated with increased symptoms, reduced lung function and local and systemic inflammation. It is not clear what causes these exacerbations and there is some uncertainty as to the efficacy of both acute and long-term antibiotic treatment. Big Data analytics combined with machine learning is an emerging field with the potential to transform our ability to predict outcomes of change in ecosytems.

In this project, we will develop and use machine learning tools to model and predict how perturbation in airway microbiome structure and function relates to exacerbation onset and patient recovery with treatment. Clinical, microbiological and inflammatory data on exacerbations, collected in multiple clinical studies, will be merged and used to build applications and analytical tools that will personalise exacerbation treatments and ensure optimal outcomes for patients.

Funding Notes

Applicants should have a 1st or 2.1 honours degree (or equivalent) in a relevant subject. Relevant subjects include Computer Science, Bioinformatics, Pharmacy, Molecular Biology, Pharmaceutical Sciences, Biochemistry, Biological/Biomedical Sciences, Chemistry, Engineering, or a closely related discipline. Students who have a 2.2 honours degree and a Master’s degree may also be considered, but the School reserves the right to interview only those applicants who have demonstrated high academic attainment. “The studentship will be funded by the Department for the Economy. Please read information on eligibility criteria: View Website. However, there may be flexibility to fund a small number of exceptional International applicants”.

How good is research at Queen’s University Belfast in Allied Health Professions, Dentistry, Nursing and Pharmacy?
Pharmacy

FTE Category A staff submitted: 33.00

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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