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Queen’s University Belfast PhD Projects, Programs & Scholarships

We have 58 Queen’s University Belfast PhD Projects, Programs & Scholarships

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  Future retail electricity market structure and operations with increasing customer engagement
  Dr A Foley
Applications accepted all year round

Funding Type

PhD Type

SSE supply electricity and gas to around 9 million household and business accounts. SSE is one of the UK’s largest QUB - Mechanical and Aerospace Engineering PhD Project 2019-2020.
  Modelling Aircraft Fatigue Load Spectra and the Effect of Novel Aircraft Designs
  Dr A Murphy, Dr M Geron
Application Deadline: 13 March 2020

Funding Type

PhD Type

This project is funded through a prestigious EPSRC iCASE award and will be carried out within a large research team (including academics, PhDs and Post-Doctoral researchers) who conduct exciting and innovative research in collaboration with Rolls-Royce.
  Multifunctional polymer nanocomposites for energy or healthcare applications
  Prof B Chen
Applications accepted all year round

Funding Type

PhD Type

Multifunctional elastomer nanocomposites are promising in a variety of applications including stretchable electronic devices, soft robotics, actuators, sensors, wearable medical devices and soft tissue scaffolds.
  SALine INtrusion in coastal Aquifers (SALINA): Hydrodynamic Assessment and Prediction of Dynamic Response
  Dr G A Hamill
Application Deadline: 13 March 2020

Funding Type

PhD Type

Currently 53% of people live on the 10% of the earth’s surface that is within 200km of the coast. Meeting the needs of coastal communities forms one of the major challenges facing society in the 21st Century and in particular an engineering solution is needed to the provide sustainable fresh water supplies.
  3D printing of drug delivery implants
  Dr P Boyd, Prof K Malcolm
Applications accepted all year round

Funding Type

PhD Type

Additive manufacturing (AM) or 3D printing (3DP) encompasses a wide range of. processes that create structures through deposition or binding of materials in successive.
  A randomised pilot study of a theory-based intervention to improve appropriate polypharmacy in older people in primary care (PolyPrime)
  Prof C Hughes, Dr H Barry
Applications accepted all year round

Funding Type

PhD Type

Historically, polypharmacy was viewed negatively (too many medicines), but the advent of multimorbidity and the plethora of treatment guidelines have led to prescribing of multiple medicines, particularly in the older population.
  Adaptation of Pseudomonas aeruginosa in people with bronchiectasis during chronic inhaled treatment
  Dr L Sherrard, Dr M Tunney
Applications accepted all year round

Funding Type

PhD Type

Some individuals with bronchiectasis have respiratory infections with Pseudomonas aeruginosa which may lead to a worse clinical trajectory.
  Antimicrobial prescribing in hospice and palliative care
  Dr C Parsons, Dr D Gilpin
Applications accepted all year round

Funding Type

PhD Type

Terminally ill patients are at high risk of infections and antimicrobial exposure near the end of life. However, the evidence-base to guide prescribers in the use of antibiotics for the treatment of infections in patients receiving hospice and palliative care is currently lacking.
  Clinical application of machine learning algorithms to determine appropriate antibiotic use in children with Gram-negative bacteria infections
  Dr Y Hsia
Application Deadline: 15 November 2019

Funding Type

PhD Type

Digital health is a growing and productive field in recent years. The application of machine learning (ML) techniques in electronic healthcare databases has been successfully used for clinical diagnosis, outcome prediction, and disease progression.
  Clinical application of machine learning algorithms to determine appropriate antibiotic use in children with Gram-negative bacteria infections
  Dr Y Hsia, Prof C Hughes
Application Deadline: 31 January 2020

Funding Type

PhD Type

Digital health is a growing and productive field in recent years. The application of machine learning (ML) techniques in electronic healthcare databases has been successfully used for clinical diagnosis, outcome prediction, and disease progression.
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